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    "# Chapter 4\n",
    "______\n",
    "\n",
    "## The greatest theorem never told\n",
    "\n",
    "\n",
    "This chapter focuses on an idea that is always bouncing around our minds, but is rarely made explicit outside books devoted to statistics. In fact, we've been using this simple idea in every example thus far. "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Law of Large Numbers\n",
    "\n",
    "Let $Z_i$ be $N$ independent samples from some probability distribution. According to *the Law of Large numbers*,  so long as the expected value $E[Z]$ is finite, the following holds,\n",
    "\n",
    "$$\\frac{1}{N} \\sum_{i=1}^N Z_i \\rightarrow E[ Z ],  \\;\\;\\; N \\rightarrow \\infty.$$\n",
    "\n",
    "In words:\n",
    "\n",
    ">   The average of a sequence of random variables from the same distribution converges to the expected value of that distribution.\n",
    "\n",
    "This may seem like a boring result, but it will be the most useful tool you use."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intuition \n",
    "\n",
    "If the above Law is somewhat surprising,  it can be made clearer by examining a simple example. \n",
    "\n",
    "Consider a random variable $Z$ that can take only two values, $c_1$ and $c_2$. Suppose we have a large number of samples of $Z$, denoting a specific sample $Z_i$. The Law says that we can approximate the expected value of $Z$ by averaging over all samples. Consider the average:\n",
    "\n",
    "\n",
    "$$ \\frac{1}{N} \\sum_{i=1}^N \\;Z_i $$\n",
    "\n",
    "\n",
    "By construction, $Z_i$ can only take on $c_1$ or $c_2$, hence we can partition the sum over these two values:\n",
    "\n",
    "\\begin{align}\n",
    "\\frac{1}{N} \\sum_{i=1}^N \\;Z_i\n",
    "& =\\frac{1}{N} \\big(  \\sum_{ Z_i = c_1}c_1 + \\sum_{Z_i=c_2}c_2 \\big) \\\\\\\\[5pt]\n",
    "& = c_1 \\sum_{ Z_i = c_1}\\frac{1}{N} + c_2 \\sum_{ Z_i = c_2}\\frac{1}{N} \\\\\\\\[5pt]\n",
    "& = c_1 \\times \\text{ (approximate frequency of $c_1$) } \\\\\\\\ \n",
    "& \\;\\;\\;\\;\\;\\;\\;\\;\\; + c_2 \\times \\text{ (approximate frequency of $c_2$) } \\\\\\\\[5pt]\n",
    "& \\approx c_1 \\times P(Z = c_1) + c_2 \\times P(Z = c_2 ) \\\\\\\\[5pt]\n",
    "& = E[Z]\n",
    "\\end{align}\n",
    "\n",
    "\n",
    "Equality holds in the limit, but we can get closer and closer by using more and more samples in the average. This Law holds for almost *any distribution*, minus some important cases we will encounter later.\n",
    "\n",
    "##### Example\n",
    "____\n",
    "\n",
    "\n",
    "Below is a diagram of the Law of Large numbers in action for three different sequences of Poisson random variables. \n",
    "\n",
    " We sample `sample_size = 100000` Poisson random variables with parameter $\\lambda = 4.5$. (Recall the expected value of a Poisson random variable is equal to its parameter.) We calculate the average for the first $n$ samples, for $n=1$ to `sample_size`. "
   ]
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9SPXu98ZAAXBnkLo8zoJ9/xbwhU+ecukI1ALyget80ocBB33S3sYa2L8D/AzU9Xrmdhau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wcFpW6uff+5BOMp/vJfTwBw+Q1LePjPu7jubY08dv9egsMxZs8rweOzs+GqBezf2c0jf8n8m9/1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\n2EYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe30f51f7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from IPython.core.pylabtools import figsize\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "figsize(12.5, 5)\n",
    "import pymc as pm\n",
    "\n",
    "sample_size = 100000\n",
    "expected_value = lambda_ = 4.5\n",
    "poi = pm.rpoisson\n",
    "N_samples = range(1, sample_size, 100)\n",
    "\n",
    "for k in range(3):\n",
    "\n",
    "    samples = poi(lambda_, size=sample_size)\n",
    "\n",
    "    partial_average = [samples[:i].mean() for i in N_samples]\n",
    "\n",
    "    plt.plot(N_samples, partial_average, lw=1.5, label=\"average \\\n",
    "of  $n$ samples; seq. %d\" % k)\n",
    "\n",
    "\n",
    "plt.plot(N_samples, expected_value * np.ones_like(partial_average),\n",
    "         ls=\"--\", label=\"true expected value\", c=\"k\")\n",
    "\n",
    "plt.ylim(4.35, 4.65)\n",
    "plt.title(\"Convergence of the average of \\n random variables to its \\\n",
    "expected value\")\n",
    "plt.ylabel(\"average of $n$ samples\")\n",
    "plt.xlabel(\"# of samples, $n$\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the above plot, it is clear that when the sample size is small, there is greater variation in the average (compare how *jagged and jumpy* the average is initially, then *smooths* out). All three paths *approach* the value 4.5, but just flirt with it as $N$ gets large. Mathematicians and statistician have another name for *flirting*: convergence. \n",
    "\n",
    "Another very relevant question we can ask is *how quickly am I converging to the expected value?* Let's plot something new. For a specific $N$, let's do the above trials thousands of times and compute how far away we are from the true expected value, on average. But wait &mdash; *compute on average*? This is simply the law of large numbers again! For example, we are interested in, for a specific $N$, the quantity:\n",
    "\n",
    "$$D(N) = \\sqrt{ \\;E\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=1}^NZ_i  - 4.5 \\;\\right)^2 \\;\\;\\right] \\;\\;}$$\n",
    "\n",
    "The above formulae is interpretable as a distance away from the true value (on average), for some $N$. (We take the square root so the dimensions of the above quantity and our random variables are the same). As the above is an expected value, it can be approximated using the law of large numbers: instead of averaging $Z_i$, we calculate the following multiple times and average them:\n",
    "\n",
    "$$ Y_k = \\left( \\;\\frac{1}{N}\\sum_{i=1}^NZ_i  - 4.5 \\; \\right)^2 $$\n",
    "\n",
    "By computing the above many, $N_y$, times (remember, it is random), and averaging them:\n",
    "\n",
    "$$ \\frac{1}{N_Y} \\sum_{k=1}^{N_Y} Y_k \\rightarrow E[ Y_k ] = E\\;\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=1}^NZ_i  - 4.5 \\;\\right)^2 \\right]$$\n",
    "\n",
    "Finally, taking the square root:\n",
    "\n",
    "$$ \\sqrt{\\frac{1}{N_Y} \\sum_{k=1}^{N_Y} Y_k} \\approx D(N) $$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nc/DgQY4cOeI6lq+vL9deey0fffSR641RmzZt+OSTT1zLBR33vvvu45NPPnGd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\nGcT+P/bzcJyV4UrlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb870ede828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(12.5, 4)\n",
    "\n",
    "N_Y = 250  # use this many to approximate D(N)\n",
    "N_array = np.arange(1000, 50000, 2500)  # use this many samples in the approx. to the variance.\n",
    "D_N_results = np.zeros(len(N_array))\n",
    "\n",
    "lambda_ = 4.5\n",
    "expected_value = lambda_  # for X ~ Poi(lambda) , E[ X ] = lambda\n",
    "\n",
    "\n",
    "def D_N(n):\n",
    "    \"\"\"\n",
    "    This function approx. D_n, the average variance of using n samples.\n",
    "    \"\"\"\n",
    "    Z = poi(lambda_, size=(n, N_Y))\n",
    "    average_Z = Z.mean(axis=0)\n",
    "    return np.sqrt(((average_Z - expected_value) ** 2).mean())\n",
    "\n",
    "\n",
    "for i, n in enumerate(N_array):\n",
    "    D_N_results[i] = D_N(n)\n",
    "\n",
    "\n",
    "plt.xlabel(\"$N$\")\n",
    "plt.ylabel(\"expected squared-distance from true value\")\n",
    "plt.plot(N_array, D_N_results, lw=3,\n",
    "         label=\"expected distance between\\n\\\n",
    "expected value and \\naverage of $N$ random variables.\")\n",
    "plt.plot(N_array, np.sqrt(expected_value) / np.sqrt(N_array), lw=2, ls=\"--\",\n",
    "         label=r\"$\\frac{\\sqrt{\\lambda}}{\\sqrt{N}}$\")\n",
    "plt.legend()\n",
    "plt.title(\"How 'fast' is the sample average converging? \");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the expected distance between our sample average and the actual expected value shrinks as $N$ grows large. But also notice that the *rate* of convergence decreases, that is, we need only 10 000 additional samples to move from 0.020 to 0.015, a difference of 0.005, but *20 000* more samples to again decrease from 0.015  to 0.010, again only a 0.005 decrease.\n",
    "\n",
    "\n",
    "It turns out we can measure this rate of convergence. Above I have plotted a second line, the function $\\sqrt{\\lambda}/\\sqrt{N}$. This was not chosen arbitrarily. In most cases, given a sequence of random variable distributed like $Z$, the rate of converge to $E[Z]$ of the Law of Large Numbers is \n",
    "\n",
    "$$ \\frac{ \\sqrt{ \\; Var(Z) \\; } }{\\sqrt{N} }$$\n",
    "\n",
    "This is useful to know: for a given large $N$, we know (on average) how far away we are from the estimate. On the other hand, in a Bayesian setting, this can seem like a useless result: Bayesian analysis is OK with uncertainty so what's the *statistical* point of adding extra precise digits? Though drawing samples can be so computationally cheap that having a *larger* $N$ is fine too. \n",
    "\n",
    "### How do we compute $Var(Z)$ though?\n",
    "\n",
    "The variance is simply another expected value that can be approximated! Consider the following, once we have the expected value (by using the Law of Large Numbers to estimate it, denote it $\\mu$), we can estimate the variance:\n",
    "\n",
    "$$ \\frac{1}{N}\\sum_{i=1}^N \\;(Z_i - \\mu)^2 \\rightarrow E[ \\;( Z - \\mu)^2 \\;] = Var( Z )$$\n",
    "\n",
    "### Expected values and probabilities \n",
    "There is an even less explicit relationship between expected value and estimating probabilities. Define the *indicator function*\n",
    "\n",
    "$$\\mathbb{1}_A(x) = \n",
    "\\begin{cases} 1 &  x \\in A \\\\\\\\\n",
    "              0 &  else\n",
    "\\end{cases}\n",
    "$$\n",
    "Then, by the law of large numbers, if we have many samples $X_i$, we can estimate the probability of an event $A$, denoted $P(A)$, by:\n",
    "\n",
    "$$ \\frac{1}{N} \\sum_{i=1}^N \\mathbb{1}_A(X_i) \\rightarrow E[\\mathbb{1}_A(X)] =  P(A) $$\n",
    "\n",
    "Again, this is fairly obvious after a moments thought: the indicator function is only 1 if the event occurs, so we are summing only the times the event occurs and dividing by the total number of trials  (consider how we usually approximate probabilities using frequencies). For example, suppose we wish to estimate the probability that a $Z \\sim Exp(.5)$ is greater than 10, and we have many samples from a $Exp(.5)$ distribution. \n",
    "\n",
    "\n",
    "$$ P( Z > 10 ) = \\frac{1}{N} \\sum_{i=1}^N \\mathbb{1}_{z > 10 }(Z_i) $$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0061\n"
     ]
    }
   ],
   "source": [
    "import pymc as pm\n",
    "N = 10000\n",
    "print(np.mean([pm.rexponential(0.5) > 10 for i in range(N)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What does this all have to do with Bayesian statistics? \n",
    "\n",
    "\n",
    "*Point estimates*, to be introduced in the next chapter, in Bayesian inference are computed using expected values. In more analytical Bayesian inference, we would have been required to evaluate complicated expected values represented as multi-dimensional integrals. No longer. If we can sample from the posterior distribution directly, we simply need to evaluate averages. Much easier. If accuracy is a priority, plots like the ones above show how fast you are converging. And if further accuracy is  desired, just take more samples from the posterior. \n",
    "\n",
    "When is enough enough? When can you stop drawing samples from the posterior? That is the practitioners decision, and also dependent on the variance of the samples (recall from above a high variance means the average will converge slower). \n",
    "\n",
    "We also should understand when the Law of Large Numbers fails. As the name implies, and comparing the graphs above for small $N$, the Law is only true for large sample sizes. Without this, the asymptotic result is not reliable. Knowing in what situations the Law fails can give us *confidence in how unconfident we should be*. The next section deals with this issue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Disorder of Small Numbers\n",
    "\n",
    "The Law of Large Numbers is only valid as $N$ gets *infinitely* large: never truly attainable.  While the law is a powerful tool, it is foolhardy to apply it liberally. Our next example illustrates this.\n",
    "\n",
    "\n",
    "##### Example: Aggregated geographic data\n",
    "\n",
    "\n",
    "Often data comes in aggregated form. For instance, data may be grouped by state, county, or city level. Of course, the population numbers vary per geographic area. If the data is an average of some characteristic of each the geographic areas, we must be conscious of the Law of Large Numbers and how it can *fail* for areas with small populations.\n",
    "\n",
    "We will observe this on a toy dataset. Suppose there are five thousand counties in our dataset. Furthermore,  population number in each state are uniformly distributed between 100 and 1500. The way the population numbers are generated is irrelevant to the discussion, so we do not justify this. We are interested in measuring the average height of individuals per county. Unbeknownst to us, height does **not** vary across county, and each individual, regardless of the county he or she is currently living in, has the same distribution of what their height may be:\n",
    "\n",
    "$$ \\text{height} \\sim \\text{Normal}(150, 15 ) $$\n",
    "\n",
    "We aggregate the individuals at the county level, so we only have data for the *average in the county*. What might our dataset look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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fj40bN3LXXXfx7W9/m74+47yNJFfiK0M4HOa8887jnHPOYfv27axcuZKnn346\np3rOP/98Pv3pT/O1r32NxsZG/ud//oetW7fywAMP8MILL9DY2MgTTzxBXV3dGDU5dWgjXqPRaDQa\njSZO6x9foOTghXiOWTFovYgw98ufI9YfpH3tK2OuZ/369XR2dnLZZZdhtVqpq6tjzZo1PPHEEwBU\nV1dz2223cdFFF/Gd73yHe++9F6fTOWyZp59+OocffjgADoeDhx9+mJtvvpmSkhJcLhdf//rXk+UD\n2O12vvWtb2G1Wjn77LPp7OzkK1/5Ck6nk8WLF7No0SLeeecdAB588EGuueYaamtrsdlsXHHFFfz+\n978nFotllMVut3PFFVdgtVo5+eSTcblcbNmyBWBEuRK8/vrrRKNRvvjFL2K1WvnkJz/JYYcdlnM9\n6VitVsLhMJs2bSISiTBr1izq6+uHPadmRsfEmwQd12gutD7Mg9aFudD6MBdaH+NP1N+Po9qTcZuj\ntgqAiC8w5nqamprYu3dvMhxGKUUsFuPoo49O7nPqqady5ZVXMn/+fI444ogRy0zNM97R0UEgEOCE\nE05IrovFYqTOD1ReXp70ehcVGXPtVFVVJbcXFhbi9xsDepubm1mzZk0yzl4phc1mo62tjdra2iGy\nlJeXD4rJLyoqwu/35yRXgpaWFg466KBB62bOnJlTPZmYO3cuN998M7fccgvvv/8+H/vYx7jxxhsz\nyj8dyMmIF5E7gYeUUm9PsDwajUaj0Wg0U4b7A/NpfeZvRPwBClyDPd/tfzU88OMxO/zMmTOZM2cO\n//jHP7Luc+ONN7Jw4cJk6MfKlSuB7BlZUtd7PB6cTicvv/zyuBipM2fO5O67786pMzEc+chVW1vL\n3r17B63bvXs3c+fOzamuTOdp5cqVrFy5Ep/Pxze/+U1uuOEGfvKTn+TeABORaziNFXhORN4RkStF\nZNZECnUgouMazYXWh3nQujAXWh/mQutj/Km74N+I+gK8c9kPifYHk+v73t3Clh/+N8XLFlD2obHn\nJl+xYgVut5sf/ehHDAwMEI1G2bRpE2+99RYAL7/8Mr/+9a+57777uOeee7jqqqtoaWkBDG/5nj17\nCIfDWcsXEdasWcPVV19NR0cHYMwK+te//nVU8l5wwQXcdNNNNDc3A4an/9lnn827nHzkOvzww7Fa\nrTzwwANEo1GeeeYZ1q9fn3Nd1dXV7Nq1K7m8detW/v73vxMKhbDb7RQWFk55pqGxkJMRr5S6FJgB\nXAUsBzbUVTH/AAAgAElEQVSJyF9E5DwRcU+kgBqNRqPRaDSTRdlhS1n4nYtoefIv/O3QM3j7S9/l\nHysv4eUTz0dE+OBPvjcuhp/FYuHRRx9lw4YNHHrooSxcuJBvfOMbeL1evF4vF198Mf/5n/9JTU0N\nRx11FGvWrOGSSy4B4Nhjj2Xx4sUsXryYhQsXZq3j+uuvp6GhgVNOOYU5c+awcuVKtm3blnX/9Hal\nLn/lK1/h4x//OCtXrqS+vp7TTjstL4M6tazrrrsuJ7lsNhu//OUvefjhh5k7dy6PP/44p556Kg6H\nI6d6zj33XN577z0aGho477zzCIVCfO9732PBggUsWbKEzs5Orr322pzbYDYkUwzSiAeJLAUeAQ4G\nAsCvgeuUUrvHV7yRWbt2rUof5KDRaDQajUYDhpc3NVY8V7pefZvGnz9B3zubsRY6qDn9OGaf/284\nqiomQEpNrpx88sn8x3/8B5///OenWpRxJdt1un79ek488cSMvcacB7aKSAnwGeBc4BDgCeBioBG4\nDHg2vl6j0Wg0Go1mWlNx1HIqjlo+1WIc8Lz88svMnz8fj8fDY489xqZNmzjxxBOnWixTkFM4jYg8\nDuwGzgbuA2Yopb6klHpJKdUEfAvIbZTBNEHFFB0tXnZt7aCjxZtx1PR4ouMazYXWh3nQujAXWh/m\nQutDs7+zZcsWjj32WObOncu9997Lgw8+SHV19VSLZQpy9cS/ClyilGrJtFEpFRORmvETa+rpbPPx\n/jv7mruQWqpqi6dQIo1Go9FoNJoDi/PPP5/zzz9/qsUwJblmp/loJgNeRH6X+F8pNfakqSbC7wsO\nWg6kLY83OtevudD6MA9aF+ZC68NcaH1oNAcuuRrxJ2RZf/w4yWE6XG7HsMsajUaj0Wg0Gs1UMawR\nLyI3iMgNgD3xf8rvV8Cu4Y6fznhq3CxcVsusOeUsWlaLp2ZiM2nquEZzofVhHrQuzIXWh7nQ+hgZ\nq9VKILBfBQto9iOUUnR2dg6bNjMbI8XEz47/taT8D6CAJuD6vGs0CSqm6Gzz4fcFcbkdeGrcg3KL\nikg8Bn784+Az1a3RaDQajWb8qa6upq2tjZ6engmtp7e3l9LS0gmtQ5M700UfSilKS0txu/O3BXPK\nEy8iX1RK/XQ0wk00o80T39HiHTxwddnkDVydyrqHY6SOjUaj0Wg0Go1m8hhznnil1E9FpBRYBLjT\nto1u/t4pJvPA1ckxpKey7uHQGXk0Go1Go9Fopge55om/ANgD/AH4WcrvgQmTbIKZyoGrmeo2Q1zj\nZGfkMTNm0IfGQOvCXGh9mAutD/OgdWEuDgR95Jon/mbg00qpZydSmMlCxRSgqKotJhqNUVlTPKlx\n6Z4aNwupJZAaE7910qrPis7Io9FoNBqNRjM9yDUmvhVjltboxIuUH6OJiTdrTPpUo5Sio9U3qHOh\nY+I1Go1Go9FopobhYuJzzRN/C3CNiOS6/6Sya2sHHS1ecumQgA4byUYiI0/9/Eoqa4u1AZ+Giik6\nWrx5X28ajUaj0Wg0402uRvk3gWsAr4g0pv4mULacad7ZzfvvtNDR6s3JyMo1bGQyjbYDIXZrOpFJ\nH4mBv/uuN98USHbgoe8Nc6H1YS60PsyD1oW5OBD0kWtM/LkTKsUYsViFApuVph3dRKMxIuEosajK\nml0lY0x6BjJla6msdus0jOPMdEltadasQmZguuhQo9FoNJr9hZxi4s3M2rVrVTRQzvZNbRQ6bQwE\nwjQsriIUjDJrTjn18ytHVa6KKbZsaqVtTx+OIhuRUJQyjxNrgYVdWzpxuu3A4Hh6bciMjukyRiFd\nzkXLaqk0oZxTwXTRoUaj0Wg004kx54kXkRuybVNKXTtawcYLh8NKRZXhTe/3hwkORBCRMWVX6Wzz\nsWtLJ51tPkRgVoMHnzdIsD9MZ5sPcON02wd5Y3We9dExXTzcuX7BORCZLjrUaDQajWZ/IdeY+Nlp\nv8OBy4F5EyRXXsyYXY7TbcfptuOpdlM9o4RFy2qHGFmZYtyzxb37fcFkecWlhRQ5bUTCURxFNkQg\nHIoAg+Ppfd4BAr4QvV0BAr4Qft9Azm04EGK3smHG1JaZ9KEH/mZnInV4IN8bZkTrw1xofZgHrQtz\ncSDoI9cZW/89fZ2InAZ8ftwlGgWZPKSZDKxMnnKBjN7zhBFihM3Y8VS6aN7VTYQoDYurcJcUUpWW\nX14Qutp9KAUigKrRITY5oD3c0x+tQ41Go9FoJpdRx8TH0012K6VKx1ek/MgnT/yurR007+xOLs+a\nUw4wZF39/MpBOdOdLgeIsWwtsFDhceKpGeqJbdzWQeuePoIDERxFNmoOKsbpcow6VljFjDo72rzD\n1psvumOh0Wg0Go1GY37GIya+IW2VE1gNNI1RtknF5bZjLywg2B/GUWTD5bYDkraP4YFPhE5AcXzQ\nXmtyn3KPK6PR63Q5CAWjiAihgQhOl2NIrLDfO4BATgZ0Z5uPt19rTMblNyyqwu8NQjzef7TGt47d\n12g0Go1Go5ne5BoTvxXYEv+7FXgV+Chwfq4VicjPRKRVRP6Vsu46EWkWkfXx32nx9SeJyBsi8k8R\neV1ETsi5RRlIxL13dfpp29tHX08/PR0BFGKEASyrZWZ9GbPqywn4g0Nywuc6OVSirFlzypMx+emx\nwYJkzDWeKXbL7wsmY++VAp83SNPO7jHnKdeTXY3MgRBLN13QujAXWh/mQuvDPGhdmIsDQR+5xsSP\nx0ytvwDuBn6Ztv4OpdQdaevagU8qpVpEZCnwHDBrtBUnPM9KKbzdA3iq92WWkdpiqmqLs8bGQ+6D\n9lK99wnSY4UD/tyzeLjcDmz2gnjZUGCz4iiyERqIjHjscJhxIKlGo9FoNBqNJndynewJESkAjgZm\nAs3AK0qpSK7HK6XWiUh9pqIz7PvPlP/fFZFCEbEppcK51pdKwvM8OLOMfZDxOlyKvLEM2hMRY4Ko\nRB3KmJwqFjU8/QkZjjnmmCHHemrcLD+qjo5WIya+0FHA7qae5PbRGt9mHYRoplj9TPrQTA1aF+ZC\n68NcaH2YB60Lc3Eg6CPXmPjFwB+AIow4+NnAgIh8Sim1aYwyXCIia4A3gMuUUr1pdX8aWJ+vAZ9q\nEBKPjImEs2eWGc47ncnDng/pMegz68uSeeyHM6AT9SYnk1KKIrcjJ+N7OIM4U3vG04AebVkTGatv\npg7C/oI+pxqNRqPRTB25hsn8BPhvYLZS6sNKqVnAffH1Y+EnQINSajnQAgwKq4mH0vwA+FK+BScM\nwuad3exp7mFmfRkzZpcxd0EViw85KJnnOxEvHwgEqaotxl3qYFZ9ORXVrjE2bR/pXn4RGZJrPJfY\nrXzylKe2P5f4+Xz3n4iyJjJWP1+ZDoRYurEyntfMcGhdmAutD3Oh9WEetC7MxYGgj1zDaZYDJ6vB\n+SjvAr4zlsqVUu0piz/F8PYDICKzgN8Ba5RSO7OV8fjjj/PAAw9QV1cHQGlpKQcffDCzaxcDsOHd\nNwGYMfsk6udXGkrduu8zyzN/fJ6mHV2sWHEk2ze1sbdzC4VFBaxc9QmqaouTF0Fi/9Es93YFKHPO\nTcrTE6igfv4pg/ZPMB71AUPaP2vOSUD29uS7/3DLrbt7qfUsTJbX0lnMylWfGPF4l9uRrP/gpStw\nuR1Tdj4SjFf9++Oy3xccpK+AL8i6df8c9/o2bNhgivbqZa0PMy5rfZhnecOGDaaS50Bfnq76SPzf\n2NgIwIc+9CFOPPFEMpFTnngReQe4VCn115R1JwA/VkotHbGAfcfMAf6glDo4vlyrlGqJ//9N4HCl\n1GoRKQP+BlyvlHpyuDKz5Yk30kLuC81YtKyWygyhGYnc8Uopmnd0U1JWSGmFM5kvfjxIzTk/WWEH\nubZ/tPuPZ90Jxvs8pYdU7WnuSY5FGEv79ifGEhIznteMRqPRaDSaoYw5TzxwNfB7EfkjsAuoBz4B\nnJurECLyCHA84BGRRuA64AQRWQ7EgJ3Al+O7fxWYB1wrItdhRLWfopTqyFR2JkMkMXjTyMsu8dSR\nJI2UxDHBYISAL0RZpRMRktlgxjNjy1hj6rORaIMv3kaxGLnqU9uf6+DV4fbP19Ab7cDZ8T5PoxmL\ncKDFeY9lHIJZB0hrNBqNRnMgkJMRr5T6vYgcBnwWmAG8A1yrlNqca0VKqdUZVv8iy743AzfnWnY2\nQyQ1daTFKhTYrLS3eqmqKQYU77/TisUqlHmKKC51cNTxDSgBt7twXAySfAzCdevWJT+p5Eqi3QFf\niK52Hw2LqwgFo4PaP5JBnC5j3TzPEBnzNfQmqtOSL9nGIgxHoq0b3n2Tg5eu2O8nwhouK9NITJae\nR3NvaCYOrQ9zofVhHrQuzMWBoI+cjHgRcQA7lFI3payziYhDKTXlMwUNZ4gkthXYrGzf1EZxaSFd\n7f6kYRaLKkLRKEVF9lGHz2Qz1id6ZtRE22KxGJ5qN8GBCI4iG/3+3A2xXGTMdn7N7rUeTT78sRi1\n0xE9Z4BGo9FoNNOTXMNpnge+jTFTa4IVwA8xQmSmlOEMkcT/wf4wSu0Ll4lGY8OWkQ/ZDOF8DMJ8\neosJ4zngD2EvLKC0wMmG15so87iIRWNU5/EVIRcZs53fie6kJBhtZ2E04R6Jth28dAUWq4Ayxk2Y\nsZMyHkyHkJj93ZMy3dD6MBdaH+ZB68JcHAj6yNWIPxh4LW3dP4APjq84o8MwRKrpaPUT8AXxeYNU\nVLsQBAVUVBnpIkPBCI5CGwCVNcVU1hSPi/GSyRBWMTco6OkOIAoQmFVfjlJqzIZgqvEc8IWomVnM\njPpyI6bfVoDKo/hcPLHZDL3J8loP6SyoWkQY0agfLtwjvWNQUeWiq91PwB9kVn0ZSkCU0Lyre1+9\n+2FozUTPGaDRaDQajWZiyNWI7wVqMHK5J6gB/OMu0WhQ0N3Rzz//0YjVamXbe22Awl1cyOYU469h\nUdWggY2GYTJ2oyyTIdzZ5mNPcw9lFS56uwLUziphT3MPzmJHRkMwEbuViwGVajw73XZs9gLKKpzJ\ndW53Yc6y5+KJzWYMu9wOUBDwhwiHI+PWSUknvbPQ0ealq33fpTca4zq9YzCrvozmXftmw+0O7ODg\nJYOzHu3voTUJJusLS64cCHGN0wmtD3Oh9WEetC7MxYGgj1yN+CeAR0TkUmA7RuaYO4DHJkqwfOhs\n89G0o4vergFEoLzSSV9PP2B4qsOhiBFGI4wq7j0WiRnl9wQoLLLjKLLichUmvbc+3wAz68uxpGSH\nadzWSSyq6O0K0NczQElZISIyoiGYMKDSB+KmGvODOg0KnE471loL0WiMyrSZaNPJ1EkYrSfWU+Nm\nlreMpp3dlBU5B3VSxtObm95JshYMnqMsH+M6Ideepm4CvhBOtx2A3vj1kiDYHz5g48Unc1yA9vpP\nX7TuNBqNZmrJ1Yj/DnA7RgiNAxgAfg78vwmSKy/8viBFTjsioJQR715aZnimuzt9iFiIRvupH/CM\nylPctKOLV/+2DYvVQk+nnyWHzQLVM8R7uzAlT3bC4LPZCxABR5GN0EAkuT79BfiRj3wk2RYYOhA3\n1Rua6j1Pz39eWTP8TK4drT7efq0x2bFZflTdEC9rZ5uXHVs7CfaHcRTZUCiqakuGFqYgGIoOWpUw\n+IZ6c2sQZNALH0XWFJmpbUj/WgCK9r3e5PZ8jOuEXPbCArrafYAbp8tOYZGdvp4BHEU2IuEoxx13\n7LSIF58IJrPzkovXf3/3pEw3Evow2xcbs3cqJko+fX+YB60Lc3Eg6CPXFJMDwFdF5BKgEuhQucwS\nNUm43A7EAktXzGIgEGL23Apmz6ugeUcX85fU0tLci91uZceWdsorXXm/aHp7AigFkUiUWAwGAiEK\ni2xDvLepHstknnrfAKiaQV56MF6AWza1UmCzEuzvYpa3gvr5nqwDcVPLTg1v2bW1I2nAp++X6aXR\n0eals82X3H9vY/dgI1VB614vG9fvxmKxYHdYKS0rymjEd7b52LWlk842HyLQsLgqKX+6N7e7MzDI\n8F5IbTL9Z7YUmQnSw3mUUixERmVcJ+SKhKPMW1KNxWLBUViAt2+AUDBCX08/S5fPSL5gzZAqc7KZ\nzM5Lvl7/bGMZMhlGuRhNZjf8zIzZMjmZrVORjtnl02g0049cPfEAxA339gmSZdR4atwoGDLTp9Pl\nIODroq/bMLY91e4hLxrjJe6lqzNANBKLD3gd/CIvLTMmgrJarVgsUOS0o5SitMyJ3xeKG+JhUCQ9\n/QkDsCrLS83vC1Jgs7Lj/TZELLz4979zzpozmD3Pw8JltXR3+un3h5PhHi63nY4W7xBjI2E0J8Jv\nAv4QHS1ePDXujC8Na4El+cUiEo4SDEZo3tmd3C5AX08/wYGI8VUjVoDPG0yWmfCe+31BAv4QRS4b\nnmo34VAEd8m+/Prp3ttoZHA2oECKARAOGXUFByI5hRyN1rhWMZXUUYGtwEgD2uojHIrg7R2gYXGV\nMfBZhJdeemnSe/FjNSjzOX64fSez85KL1z81rnHoBF7l7N7ZnRyXsXT5DOrmV+ac4lUbVvmT0IfZ\nws3M1qlIJ+APYi8sSH7hzCcV8HAcCHG/ZmGkZ6zWhbk4EPSRlxFvVrIZHUbMdgV9Pf3YbAU43fYh\nL5rONh87tnayfVMbShmG/vKj6qisdidvVmexnSOPb6Cvp5/CIhuOogIjJr7ahQI2vr0bm62A5l3d\nWQeupt/8LredYH8YEQvdHX4G/GGadnbjLC6kqtboSJR7XMmOiYJBg3QTxkbCa9rd6WfXlk5CA5F4\n+E0NHa0+ersC2OxG2wO+IBUeJw2Lq5JGusVqAYyQmIRhbbNbKa9yEhqIYi+0EovFeP+dlkHecwB7\nYUFKR8NOVUooT7o3V8Ggwagut4NYTNHbHSDgD1HgsOJ02ekPDI1FH458DNfONh/Nu7rp94fp6+mn\nfn5l/LwwqBMxUfWPRK4G5XjMS2AW4zVfr3+6odbXY1w/ia9LqfdQLkbdWL8EHMie+9F+sZmoczjR\nnYqxd7JJvmdEyCsVsMYcTPVzUz9/NOnsF0Z8tjzexgydHlzFjqwvGr8vmAxdAcMrHPAF6YTBN+uy\nWuYsqBpStwiUlu/LDJNtIqQhRviyWmbNqaCtpY8ip52liw/FUWRLHp/eMdm1tQMwbuK+nn52bm4n\n4A0ye14FVbVGqsyE1x6M8BWfN4i3dyDeNney/Qqh3x9koD9Ce4sXe2EBkXAUl9uO3xsiFIwwb3EN\nvV0ByitdRKMxlFJ0d/opLNx3yUTCUerne3AUFgw5t5lCYEh74Tdt68Rut4LbQYFVcLrt1DV48grf\nyOehmjDYEp0Oi0VSlt1UzyihwuPCU+PmmNrceu/j+VAfyaBMXFNd8Q5bQt+jmZfA5x0YNOjb7xvI\n+tVoIsnF65/qSUn/8oSCQqeNApuFaCQ26B7KxajL1/AbbbrTqWS8X/wJfYz2i81EGUITHQY2VrmV\nKCqq3Ml7Lp9UwMMxmjlGzHy9mpmRnrET7fWd6k5EJsx8Te3vXnjYT4z4d9fvzjpIc6QXjcvtwFFk\nS4aY2OyGQZqrQZT60k9MDrRzSzuhYJQdW9qxiAWn257MVZ9Iybi3qZsZs8tYcVQ9TTu7kwMqsxkR\nifV9Pf3s3tWN3VHA9vfbUSjq51Umw0QS5UQjMSLhKA2LqwiHo5R7XPh8AwBU1rjpbIWmHUaGlt7u\nAEuXz0Bh5EUP+EL0dAaY/4Ea+nr72bbR8B71+8MsXFqdlCkWVVRUupKDebORnJwq7Sbv6Q7Q0brP\nO187u3TEstLJ5xN1+rnNNFdApofPcA+p9OvE7x1AGJ1RN5JBmXiAK6Xinmd38gtLrkZroj2hYJTO\ndi8ghEMR6udX5D3oezwf3vlkREr98lTksuHt7adungexyKB7qKLKxcz6cvp6ApSWOamodmUtb7Rf\nAsYj3elEY7YX/0SFvUx0GNhY5Xa7C5MOhMTyZGO2a2G6MdUhZGYMGZuqa8rMnYfJJGcjXkQWYUzu\nNOgtp5T6+XgLlS9+X4hYbICOVm/eF4/hmVaUlBUSDIQpctkBhSvFqw3Zb9b0TDEJI7iz3Yun2k1n\nq2FsVR1kyBXwG4M4yyqdbNnUxkGzyvBUu3l7wxucfNLxWY2IRD07N7djdxTQ02UMtu3u8NMfCLP5\n3RYKCqxIfGCms9jIahOKRrEXFtC8o3uQ5zYROpN8qSRj0fetsxdacUUdFJcWYrNbAejp6R+STjMb\niZusvdWLzxskEjbCdnzeICJQWGTHZrcQDsWMLxplzqxlZa8j90/UmQy2bHMFpObtb9zWSdPOrngH\nqQcFyess/boQZNQPtJEMysQDPNHpDPiDg/Lz52qQdrb52LGlnbIKFwOBMHUNFXS0+Siv9OV1/4zn\nw3u4slLjGhOGWuqXp9JyJ8WlhThd9kHt7mr3szs+WZe3N5gx1C1fwy+fdKepLxmny4GIwu8LTfoL\nZ7xf/GONM51qQwhGZwAMN/5oPGaQHq1Rko8+zGgETidG0uFEx2Cb4d5JZ6quqVzePzomPo6IXA1c\nC/wTCKRsUhipJqeU7g4/5VXOIS/UXBARKquLCXhDbNvUhs1WQNteLwuX1bJw2cgGUXqmGDBCcqxW\nK+GQMZgzHI5Q4XFS7nGxt6mbskonkXCUApuVjW/vprTcSW9nAIXhye9o3TeANT37Rs2MEsMDHx+Y\naimwsGtrJy1NvTiKbMysKwcRKmvcJMJXAv4QoYFIUuZEmxIvo8SgXFfx4I6L212I2210BhIZZMo8\nTra820r9fA9OV26hB71dgeTAUUSSbVZKsexDswj2hyktczJ7XkXe+svnE/VoPHWdbT7efXs3fd0D\nyQw8mbIQJc5pwD/6B1ouX43A0PusBg8Wi9GJee+dvYCibn5lTu3z+4JYxMLu3d0E+yPYCwuoOqg4\n74fveD688y0r/eVVVVM85CvORLxc8kl3mj6zcpmniFDQ6MhOpgc004t/Kr1YwxlC4yFXLmWMpgOa\nffxRbroc6f7OR6bUNvZ2BXL+ijaRRuCB4Bmd6oxlZkx7PFUdC90hNcjVE/8N4Ail1L8mUpjRMmtu\nBVW1bio8Qz25uT7Qm3Z20dc9EF9jZLExJobK/aJIzQ2v1AC1s0ooKStk9pwKPPFBn6kDQ4ORMDab\noYKDl67IGIs/s7486U0EWLC0JjnI1mYvoLc7QCQcJRKJUShCT5ffGOjX6sVT5UaAYDBCwGdkkun3\nhwkGIzjdDg6aVTZoUG62jstCapOdD29PP51tPopcNtpbvDnFoBvnA0LBKBarEA5FCfhCuEsdiAil\n5U6cLgddbb68PZXDfaIe7UtFxRSL53+QXVs7CPhD2GzGV4jE4NfUh1T6Q70jZU7jRHhVtjEb+ZL6\nAA8GIzTv6KJtj5cCm4W2Fi+BQHjIxGCZcLkdON12qmuL6evtp3Z2acbJrUYi34f3cPoYrqxMnpRc\nXma5eE7z9ZYPl+7U6NSqpL4T4WtgdOwTA6cB+v1BOlomLpZ+8Lm2D7mvO1vzN2ITZc6uXZyXBzqd\n4Qyh8fi6k0sZozEAMn0FyvXYfGfiHqnc1DaWOefS0Tr0K1qmOifSCJyIsIqJ6hhMVCaw4by+49GW\nqe5EZGKyOhaZkoOkkun9s7974SF3I74feG8iBRkLAwMhSsudVFQPvXhyfaAnQhRCwSjePiPFYuO2\nDpwuOwqJxzoPnpAoNd2iy+2gotrFwmVpueHnDr5ZPTVuFqoa2lq8hENR+nr6UcqWTInZ1elHKUWR\ny0hj2b63b9DMov3+UHKAbUdLH35fkPaWPg5eMYuOVi+lFUVsfGs3dfM8+Lwhdu/qxmIVyjxF2B0F\nOIpsRojLXi8VVa4hg3IzdVyqaotBwduvNeLt68dqs1DotBHsjwz7okncVCUVhZTHvz44imyAcTO6\nih3JAZqj9VQmHiC+vgHCoSh7d3fjjw/47Wrzj+qlknrN2AsLADHSaIYj8Q7ZYM9hR6uPjjYv1gIL\n5R4nC5fVEPCFkuFVw9Wfz4M99QHe0eJl68ZWAMo8TnZu7cTlsic9g6nZldLLHTyHgeQUGpWJbA/v\n0WTPyfdFkMvLLBfP6Vi95ek6ef+d1uS2mfXlyf9t9oLkhG/GOWJC40iHnOtltYNmqx6NETsZsa/j\n4V0L+IM4igoIh6LYbFY62734fQO43YVDUvMmyKcDO5pjczl3+YTrJM9TyhgrgUH7ZqtzoozAifCM\njjVjVyZGCpEcT7nGesx0wIhmcNPJvmtgIr7CZHqm5RItsb+TqxH/XeBuEbkeaE3doJSKZTxiEpk9\n10Nnu4/ytqETOQ2XhSNx4weDEbw9/cxq8NDbFaD6oGI2vNFEXYOHcDhGR5uXAqslGRKSmJAo1asO\nxkWVnhtexRSdrT4CgSADgQj9/SHstgK2bmwhHIpRVlFEcWkhzS3vIVKTnDypstZNd6efmXUV+2YW\nTUuRqRBiMcWCpbVEwjHcpYV4ewcIh2IEByJ4+wLJQZ8FNqvRSRmIJB/8JaWOQR2E4V5GIooyT5GR\nBSQaI3F7ZstfD/uMqI5WL2+u20kkGiMcinD4R+fi6wtS6LSRmDNskKdSQWe7l55OP/3+EIUuGxUe\nJwrLkHj2xAOku8PP+ld2YrVaUSqGIuXz8jAvukz4vAO8/sZrLGw4mEKnjbr5FRQW2jK+HDrbjBlw\nUye8mrOgivr5lcnwqgSZXmqjfbB7atwsXT6Dpp3dWAssdLb5Bk0MNiS7Ukq5I81hkCvZDOlsbfL7\ngoNCuLo7/XjSwsXq5nmG6Ga0cY25eE5TDY90b/lYw4ssFpIvGWc89CbgC+Fy25Od9cRA9PHy5maT\nJb380RiiiTI3vPtm8svheBqDqfM4jDTQf/hyYMfmDrrb/Tjddqpqi7FYjFDL+vkeKipdSYfLaAyA\nZCc47tgJxL+qjEUfqeXmEq6TOC8Bf4hXX32Z0884mR1bO2hv9Sa/xk1kuEGma3EiwipybUO+6XWH\nC7j3cXwAACAASURBVJEci1zDPavMGv4xHl8IpqqDP1K0hI6J38eD8b9fSFknGDHx1vEUaDQE+8M4\nXXY6273sbepOxldbLBYEoavdlxz4iKpJHpe48CxWobi0EItFsB9UTGebj3AoRn8ghFgsFBRYsNkL\niMVU2oREgxnOSCty23nz7zsoLLITCodZfMgMmrZ10dHqp3pGCaUVxsRRiXSHIoLdXoBYoGFRlSFj\nWRGgkvGPAV8Qi0Vo2d0LQE9ngLkLqwgFvTiKbDgcdv756nYikRgFBRaOOG4u3t5gcnBtRbWLMk8R\n7pLC5IM/G35fiFAwSqHThgCxmKKk1EFbq5d+f5hQMEIsqjIai3ubuimwWY1wlCj4+oKG8V3lojUa\nI9gfpszjwlFUwEAgbEwi5bezbVOb8SJ22Vly2Ew6W31D0iomznHTji56u4yHcnmlk76efmbMNjyh\nAX+Qnu5+KqqcNO7sor21j6qakqwPK0Hw9vTT1zNgdNwWVVPX4KGzzUfjts5BDzq/L0g4FPeuxsNt\n8skWk+uDPdODdnaDBzAyFhWXFlLksiXrmcoXRra6XW4HBTZrciByvz+M3V4wKFxsIh7+w+kh9f90\nb/lYw4ucLkc8Tn9wp75xWyfvbWiht6Mfu8M6aKbj4Ui+KOOd0oRBmnodxyIxmnZ00dcToLc7QElZ\nUUbPc/qXj4oqV9bOeLb2DSfzaAyD9HkcEjMn51ueEoXdbsXuMCZ0i0YVA/1B+v1hXCV2env6k8Zu\npk5jLm2pqi0e6sQZ5trN5dzlE65TUeViVn0ZbXu9VM0oQSnF9k1tFJcWJg1/l9s+KHNXevjBWMhk\ntFVOQFhFrtdcPs87vy+YDGPNFCI5nnKN9ZjJIJsBPp5Og/FgMs/fdBrfkasRP3dCpRgjzTu6KXBY\nKS520LbHS0zF+PAJ81iwtBaxwoKEN6zYgSXe5VAxI++5xSoUFFiwWC2UlBXS2txHcCCC3z+A3VHJ\n1k2t9AfCCDBzbnnyJT/oAlKGoRjwh3hvwx4KC41QGMFIo2h3WJO56KPRGFaLlYFAGCCZleWQBcfQ\n0WIMkHPGH76xqCIWVfT09lNW6aR9r5f2vV4WYjzsXW4HTTu6UDHo7QrgLi3EZrNy8IrZVNW6aW/1\n4iqx4y4pIhqJEY5EWbishr1NPZRVOpOGt9NlHzG1Y6K9BTYrO7d04PcGCQ1EmN1Qgc1mobTCSSia\n2aOYmPHWZrfiEjueajdVNcXElKKnI5D8SjKjrhQRIRiM0NHqBaWwFxZgLTA88IaxbLyI0r2pRU47\nKhYjElOEQ1FKypxJQ2X3rm5cJYX0B8Ls3NJBaXkRXe2BrC9dscDpnzopoVq6O/wIsKe5h1jU+HKQ\nONbldiQ94BYrOIsdyc/gqd6+9HjpfD/rd7QaHv/EuVp+VB0CNO/qwWI1rofhOmOJctMfToMHThuh\nYyOl3ByJbG3y1BjXpJHtyJiArK8nkDRKwyFD7+kzJic8KcM9WIfbNlzYDyiqaouJRmMsWFqDAB1t\nvvggeTXsgMEh5zKu735/EBXDiIlvGRri0LSzi35vCJvDitVmxeEoyJj+Mp3EizIxuVWmcSlNO7p4\n9W/bsBZYkl/5ZtaVG1m40uStrHEj8Zd147ZO3o2Pj3G67RnvjcR5nDXnpBGNtNHk00+fx4H4lzbI\nfP0n5BsaK1uI0+Wg3x/GarNgd1iJRKJGRiynnS0bWgYZu6MNg8jHcMknXCyXZ0JXu5/mXT3YCwso\nL5qDt2cgmSI5IYvT7dj3fPWHUSnhVGMlU9tlAkJ1cj1vQ89Z9i/EiTFBkDlEcixyDef1zSXl7WgZ\ni9GZ7TrOx7s+3DU72ud2OqOJvR+tF346hT7lZMQrpXZNtCBjoaSs0MjAEooS8IcAaNvrpbzSjYrC\nlndakp54z/ENyZdW4/Yu+v0h2lu8zJ5bTmWVi+oZJfh9QT5wyAzCoQgFNitlHhvRcIyaGSVUVrsH\nxQ8nwkWK+u1sfMvIutLS3Et5lYvudj+VtcU0buvksKPrsVjBarVgc1iYMbuUqlo3pWVOnMX2uHGX\nOgDNjppfmRzEmJiREvblIvf5Bqg+qIS2lj4chYaHvMhtj3tkhWgkSs1BJWzZ1IbFYiEcjlBdW8qM\n2eXJCzTXwZfGDVRDS1MPpeWG976n04/NVmDEn8bTR2Z6eM6eV4HCmKQq9SvJrq0dgwalGpNzVdLR\n4k1OVGW1WkDA6bJTUGBBrILVaqG3K8Dmd1uo8Dhxue2EQyE+eGQdAX+Iylo3rmJb0rPVtrePd/6v\nmaraErra/bhLjMGv2V66TpeDmPLS2xVg+3vtFDntlHqKOGhWKaFodNCxnhpjht+OVqMDtreph/5C\nm2EgLKtNvtTS46XTZ9wd6cHU0eYddA10tHpxuozzFosqQtHooM5YRZWLWXPK6es2UpHGlCIajrLt\n/XaadnRR5LSDKCqri9m6qRWbrYCySic9HYGMXzuGN/4HLzvdjuS4gNQ2iQhVNcWD8qqXljlp3e1N\nhiP5vMGMg/Rg+AfrcNuGD/vZp5PKGsO7mpAvtcOciUwxmlW1xXS0ZPfQJsbfhENR/L4QpRWFBENR\no/1I1pdZItSktzvw/7l7ryU5sjTP7+c6wkOrjMxEKgAJjVLdPT0zO7XdSyNnSaMwqnte8gn4Aryg\n8R14y0egMO6urZG7s7PT3dM9VV0CKAAJmYnMDC3cw7XgxfFwRCoA3TPdNrbnprKQkREex4+f833f\nX3xEUYKskBcUzjSYGy5I0hQ5hdFAoHzLNTHsWRerp2tl3jwfcfBDn1FvkVnJls++5yWB//KaVp20\nVq/5fP+G0dBi1Hu/n/77AoHL1v+V9/5hN38mVVXGMBQWiwDbCnDt4EKw+zFuTqvjUqRN5IPvbTz4\nsQHuh/aENElzOpYkwf79NXRdIQqTM9TIRV7Rv1j4+PuOP0ZVNF97K7QlepfTls7P2VUdzi977VVn\n3vsCzN9HYHqV5e3vEsgukbbZ1KFQ1DGKCqVS4b3f90Pjqnv5D5Wkvi8B/12C5T+mqPcfK/XpsnFl\nEC9J0v+Wpun/mP38vyO2qQsjTdP/4Q90bR89ak2TFLIOnCmKKlOqii6tl1kQLjlx/WMLzwnY3W+j\nqAqpBJ4bMBk6TIYOa5sVNF1BlsEo6JQrBkkMw4HFeLSg2TLzZkFLCk4QCKcYdxHgeSFhmFA0NeYz\nj5/80xvEYUxrrcJOFsgOTy2ePe7x+MnXfPLgR+iGymZWOVt6mA9PrTM2dqte5LIi8eCLLQYn85xH\nuvx9qWIwHjmiIqXKKHIJx/bZudm64G2/HFdWpyUJCYnTtxa9ozmuG7C2UcFzhSf+p3+yTadbISXl\n1cEwP7xTUjrr1TPdbtMkZXhqEfgRsyzI1HU14w2LDWE4sPjkJ1v4XkQYxAx6Fimwe73Fy2eDM3zG\nvVsd1jbq/PZv36AoCqO+RbGo01mviTmSJYqmTsHU0HWhDQARrA9P54xHDnGUZM2fyjQ7Jf6v/+Nf\nst66jW6oqLpMukKlgncbXc4vXxcWo0ZBy7/nVdxruNgU6kOwvqLKSBK5tWgURGBers5Pk5TDF2P6\np3NeHYxI4oTescXufpO/+5vXOe3o7mcb9E/muSuTqslXoh2jvs2zx72Mzz5mbaPKaGDnyMR5F6Xz\nQsrlOM8nDqOI9noZVZOpNoqkacrJ4QTH8kFKIZX49Ve/5Oc/+9kZt5cPze9VQejqAXn53/BBZGA5\nrvrM94kOS2WDKJyys99iYfls7NRxbJG4TMfOBaRnOUZ9m+OjKZ31CvbcZ2uvQRiKHhBL5AdSPCfE\nmnpousLOflNY1mbiyMuudwQcvhqTJimuEwA6YfgOabzskH1y8Fu+/PLL9x7A5/s3fPrT7Qt6iPPz\n+r5AYHX9S5L4/+W9PT6cZHMvZffM5u6nG2fE3e1ulXY3ZTJyPloHtBzvQ5ZW99HLkLrfp0K63FPS\npHwphW/Ut3PtlCSBFb7mv/nv/jPWtxsf5da0HOcdjM6jcOeNGz4G3fqHHMv1tbQ3XtWjfajfw+uD\n4YXn+GP0N6tz8rFn4+r4fTjxv0sgu0TaZEVmOlpw/0dbkE5pdsxLtX8fs/4uu5fn9SlJkrBsZHne\n4GOpTbsqwL6QgJ9a+dm3LLqen5N/qPFX//avuHf7898ZofjHSn26bLyvEv9y5eeDP/SF/H3G1l6D\nrd06w/4C3VBFw6YkzSf+vAXhkhOnKLIIiqIYo2BSKheQUik/LPrHc+5+vom7CFhYPs8e9emfzNnY\nqjMbO9y42yFFUCKW7ja6riDJsIwUJVKSJCWJE8IgFlScipELrRa2j6opWDOPr/7mDXpBpX9snclW\nzz9kjuOvCFZViqbKnU/WmWTBaBBEyIrEwvIpmhqaLqPrGqkkFuPqA/cx4svlWFZ21rdqzCYOzU4Z\na+7RbJcoljTa6xWefn965vCu1Yt01qtn3meZmZsVHUWR0Q2Vcq3AMk8UfPkK1tTj+Q99ZmOXQlHl\n8z/bxZ57RFGClHmk5xx0CUgl4kjorONY/DdNUoqmTrVZIEkTPvnpNs12iU63AqS8PBjl19taK+c0\nlTBKUFSZwI8EetJQqLdMAj+mlQnjzo+P4l5nB4vrhDz7vn9p1fuy0WwVufWwiz3zUXWFKE45Pppy\nbbeeOxst6Tqk8N3Xb5EkienIodYo4roB07GLoij5+va9kM5GlWqjKDjEBZUofKdTX73+5TpdzlX/\ndM72XpMgEujXeRelq9bRcu1JwMuDIS8e92mtlTk9mmEUNA5fjKjUCjyenbB1o8XRixFWKFyGVt1e\nrpxfzqJLpDAc2LiL4ExSufo3q24gpqnj++EHkYHVyviSgrJ8v+V/l1qMeqvIy4MhKSntboUUUUku\n1wo8f9QjDBIGpxab2/Uc6TlvQeks/Bxx0Q2VoqlTVuUzAsjOegVFk/jin+wSeBFhGGPbPk++O804\n0hfX5xIZ6L2dcf1OG1mWuXlnLV9Lvi/2kmVwuqoFel/iJBxiNKIowTSFI9V5PUSjddaI4H2BQLNl\ncuNuB9+LMIpC6L4MfvSCynTi4i1CdEPJ79nSUWu1AnjrfpdGq/Q7BZ/v1w/omGWDk8MJ9sy/sP7/\nPrD8mb9NYcuqi6Z8C2EX3FoTxanIefeZq4HKhwLt8y5c51G493H+/xhV0eX6CoMo565LSAx71ger\n6OKZCRgPbWRFZjZ2efztMaGfsLDE+bm6FyzH6pykaYq7CD+4p33suOp8+F26js+mTh6zJAl4TkCh\noBHHKceHE2RJ0MeW2r+PWX+X3cthz+Lw1ZjZxMU/nXPvk02Oj6bYM/+DCdX5sUzAAz8mSRKiMDqz\n7n7XpPr8eF+iMp+6l6KPv09i84ccfx861JVBfJqm/+vKz//zP8B1/sHG0gnEc0PBIR8uaNzpnKG8\nnMkyU4CUaqOAZihc22vk/7ZK/dB0lfnMJY4SJkMH3wsJvCgTcsZ5ALlzs0VKSq1eJEkTtq43mE89\nbtxukyQpW9ebxHFC4AvBnGP7eZVlmYnevf05r54OIIUgOGvdeKHK8MzPD0NZEYHywgryrqj2zKfe\nKoImcXo05e5n10iT5FLu38VAxuf1s+GlVpqrfvNhqAvxraZSLGlIqSTuwSLIJc9LDQCcXaS25TOb\nOIRhzNHLMe1uGXcRUK4YgtKQHUBv30zQdRVNVyiWhN6h2SkxH7sYRZUkqxIsOY47N1u4TkDR1LNm\nV+K6T46mbG7X8b2IazsNdvdFBeZ1hhhkBjmE+bzDg7tfoOsKn//ZDrIioagyT745IQwS1jZFIOY6\nAYoq02yZtLrvp8Ys6Uj9EwtZFQnWbCL6pgmLzfcfECkyo94Ca+4SxynXb4sqdxDEGIaKY/t5JTBN\nRYBpFFQ8J0DTFQIv4ta9Lmma0GibxHHC5naDR1+/BWAeJ/z45h71B5cHOKWyge+O87kqGBr23Mea\ne8zHLq31MpORTRAUkCS4ttPILFAv91xf2EJTISsyk/GCrZtNVE2m2SkTZIe25wjqw91bn4s1uuL2\ncvn8XkSXRkMbs6gx7C0uJJWXuoGwYGOrntuxXuUcs1oZD/xYCBKzwHvJjT98OUaSJV4fjIijhHqj\nKGgBtk9rrcywb1FvlSiaOp4bEIZxbo04n3k8+VYcPmEYs3+ve+bzO93KCl1CjDhOkGWZF4/7GX0q\n4c6n6wR+fAGBW52/KJyyc7OF7wl+cKmi5zSj3HYzXtLljLzSWCpfbBiXxIlAgXoWp4dTZEVG2Syj\nKAqyBJu7DcIgwihoH4WWvLu/lbxSvExYjw8nQjuUJOzebGHNPKqNYn7PltqB5VhScH7X4PP8/iuo\ncWeD31LF4PhwQqdbodoo5knk5UnQhz87TVIGPUHpE9SflMNXE7HnFtSV4FLnwRdf8vJgdAH9/FCg\nvZqE+W54CQp3dpy/9j+0+G95NhVMLddGpcmy67mgZl0VRLa6ZXb3W5RrBtbE5dHXx2xdr9M/FtTT\nJE4vLTCtzolR1JhNXbA50xn7fd/xfRzsq7U5H9d1PE1SCkVBg9Q0FVUVCPPC8lENmc2deuZ4Z5LV\nCH9vWsjCFkJwayrQz+PDKdV64WxClRt8vP/9mi0zd/7TDYUgiPG9kEq9SBhEbF1vUCxpuQXs7zou\nFVlngfr2xl16b60zidj7nNuW44/tx//3SfY/Vtj6j36UykZO+AkjceCMehatbuUCNCkBjU6JYknH\nc0KmIyc7VAI66/KKD7vFeLCgWNJptEUVttE2kWUZd/FOxDjq2YKKsVZh2LM5PZrw9S9eE8cpqirz\n5V/eZjxcnLnW1Zs2Gtg02yXSJM0D/Xf8yosw5ypFCMgoEW5ugWmWdcrVAqWKTq0uqrD1jIt+fgNa\nuhzMpy6HryZMhgqTwYKd/RZRmFCrF6g1TI6PpgC53/zSpUaSYG2jyvMnfcIgprVWptWtYBgqSRxT\nNPW8IroaWNWbRUCgHp4biqrh3OPZ96cUihpmSadaN1nfqrG5U2cyXtDslFANiQc/2mSxEL0B1q+J\n4HnYs1lYQvwqDkyJJEroZRXioqnnFYFVSswSQVnyZJcHRxKneK6Y385Ghf7xnDBIUDWZcrXAr//9\nK9I4JU0Trt/ukGYIzlUVoiUd6fXBkPnEQ9FkGs0icZycoURcdRCuOlaM+jaeIw7spc/+bOKIgDIW\nXvzS1MVzAvbvd6nWixTLOokU8fmf7uC6IfW6ieP66IaKLAudgeMGNCixc7MlEreejb3sj6BCq1Oi\nfzqnYGhoukKtWUTTFUxTx5m73H64wXTkUG0Wef18gFk2rvRcL5UNimWd6WhBkoDvRPz05zcIgxhs\nsa6Kpo4kveN+n3d7Oc/JXvK1V9GlNE4JQoGqRFGM6wRnkot2t3zBDYRsjbzPpWZhr1TGCyoHj3t5\nz4WcG9+zGJ6KIHLJgX+V90XwabRK+H4EWXJRKhsc/NBjOnRpdcvMpy6KIhFHKYNTK0ddrqoMtbti\nXio1ofmYT738sF1F4Fb3QrNssH9vDXcRkMSiKjjoW4wHNoWiRrEk9hKzpF+aONmWf6ZhnCSl9E8t\nPCfk3hfXWFge61u1XHcxnzpcv9Nh2UchTdOPO8BScltbx/Y5ej05Q7NAEoHy6j3z/egCBeeq8aGA\ndPX3qxQA3w1xFiIh39prZoiMznBgE0UxcZgiyWQo19WWmatc51rdpFjWc11QmoJZ0am3SwReRBTG\n7O63MApiv5qMFx9EPy8bq9diFDW0RXjp767yrX8/ner9GpqPCfjfJdk2zx71CfyIKE7Y3K7nr1kN\nIi98ZtukfzLn+HAmKIhhShSlKFGChJQXmC6bE1kR+/nGVp04ipEVmeOjac5jXx0fm8xcFRguz/Mk\nSag1ioyGC/wgzotDq6L40cBm+0Ybzwl48PkGZkXH90S/mXF/kRcjbz/oXvg+7+7h/IMGBqWyQRhG\n2XWLvdj3BKIVhXGGpgU5BfZ9c9HqVlgbOei6TBQlKKqCUdTyNbuYB3z+ZzsfNNe4alxFE1yidJOR\nTeAXiOKYZqeUMxV+18T6svG7JrJXvf7vw8H/DyaIb3XLbFl1XjwdUioZecOjNBOmrW44y4DHKGq8\nfTWhUitgZD7g59/zNutMR0KQ4lgBWkFhb7/N9o0mJ29EYPvy6YAHn29iVgp8/cs3yIqUw76arhIE\n0YUK4pvno/xzVFXm8dOv+ezPfkQUJtSbhbyqehnMudqldDpxkGWZKE5ydEBRZUxTv1CdNyv6BfHc\neLDg6NWUIIjon1hUawUCP8J3Q55+16NSL1CqGFzbqZOk4tDSdIVRz0aWZcyyjjVzGZxatLsVvv7l\na5rtMoapceveWs6TXoUmiwWNclVYej780RayKjHq2Tz97hRFlbl5dw0kielwAUiEYcT9z64xGtjI\nyPz1vz7AMFQkWeIv/uNbOLawpVzOiaxITEYLTt5MGGTBShjEPPjx1pl73OqWcwQljgUnfhmkTH71\nkk/u/yh7fcps4iJJUG+a9I8t5mOHKBRVbd+LGPZFwnfe/m/18DrT/TVJKdcKqJpC4MVMQ+e9bhln\nqWFl1jargqeIuCf1ZikXF0dhzIPPN3GcENsSNqTPvj2l2RG9Bm4/FFB5vyeCrcCPKZRUAj/OqRdL\nKN2xA+ZTh5v3uhw8PmVto4YsS7Q6ZaYTJ3+GNncb/PCNgKv1twobO/UzGgLBgRecZHchEAxVkbj7\n+QZRIHocFEz1TLM0SGl3y/ziV3/Nz//pz2m0TYanFk7m/uIHIa+fjS9QklbvsVkyKJga/eM5qiKz\nsHxePhueSS7OP/dCG1H5aH6x777rvAzvNuB2t0JrrYw1c4GUheVnleEyrhsSnlr0j+c4C58v/nw3\n1xhouoKuK8RRTJrKyFn1VZKkM1anlwmIV4W5mq6ytlml2Sqd+Q6XCXLNksFXv3gjRPltMxPQGpQq\nhfz7NjslRj2bf/Nv/i0///nPsgMIqrUi86nLqO9TrhmMBwuiMObg5Sl3PtnAXYgqfakqiiGDU4tK\ntcjx0ZRiSWc0sEnTlFJVFGI+1LjoLM2hjFFQ2dyu50YA7+5ZeoGCsxznD1NIefa4n2s+tqxmjtid\n//xVCoBR0EgRtrnVWoGjVxMURSL0Y249XOfVk1MKpkh6Vy0zz48l13kZiN/9bIMojLlxp4Nt+Rl6\nlubBR7NdyoOef/Ev/jVKupHNDZcGp+/r3rp0UypXjBVk8R2KfZVv/ftoIKv2zaqmcPxmgh/ERGGc\nWxF/iNawarlZzxJkxw7wnPCCNumqdb2112Q0sAm8GN1QKJoa5VqBRlPQIc8XTs6jc2EYYU1FcSyJ\n0ytRuaX169/+5pf85//lf3LB+vV8U8DVAH15nusFlVfPhjktbEnXXRXFLwPPQlHDLBvs3hRGEKO+\nna/1VcQ9/z5Dm4PHfQYnMBk6LCz/wr65OpqdEvv31uifWJgVgzCI2Nxo4jo+nY0yUSgKWgvLP6Nt\nuSqx03WVFz8McvbA3U82zriU/b6B9GXcfQmxh8iKxLff/YYfffFT+idzDEnl0VdvWd9uoOvyGXTx\n9/ncUd9m0LPyGOu8xfZlY9S3efLtqdBqhBEPPt9kZ7+dF6GX//4xqM9y/AcRxC8zy9nURdUURn2b\nKEzOeHavZjqapubB7o27ndyab8l1tM91Z9UMhVanTLEohCNm6Z0DzBKuPXw1yTmK9ZbYINJEVGHq\nDfNCBr66cDRNJY5TXj8bIUlQKHZyfqXvhgR+SMEU0P5kZLN/r5snBZ110ZgqihIKJZVGu0S5bHD0\nesJs7GDNvBzK6p9YTEdO/rlLaM9ZBIKz5kXE5YQ4ToSvO6kQA6eC1//ihwGBH1Os6DSbJkmGHNRb\nJnpBxZp6OHZIqRxl92HBbOxQbRQxihrzqQvoFEs6tYbJt785RJJkFrZHrWHiLELqTZMwjJEkCWvu\nUakWqTVM9ILC/t0uT78/RdMU4jgh8hIGfQttIjL7wI8o1wSd4/WzEdbcZdQTm1v/xKJoamcOUXFI\nVC+tWtWbZi7MTOIE2wpQFBlZJkNvFoRBQBwnGEUtr/Kdt/+7ttvg2fe9fF2kCO6964nKZ68/x165\nR+eFlMvNwrY9ru02znRXfXMw4re/OMwP/s//fJdCVp1rtEwOXwnBn67JrG9VUbVVRw4R7N+422E2\n9SiXDcZ9kZgtbLH+QVCMag2T4zcTRj0Hex5wbacBa2mWgETcetiFNM2COVE5DIOYWrNImqTIighE\nXx6MeHUwZDpyCLyQOw/XxXdMRdXbNEWlfSnIevN8xMHjHuO+m6E4aW6r9+Jxn7XNah4Um2U955E7\nC5+t3XqmAREuSsuK/rgvOgULtwshOt3crl9Ispei8qvGVfQdWAqmLdyFz+5+i/7JHM8J8b2QKIwJ\nAlFNFb0nElRVJHZGUUNSxLzPJg53Hm4QRjGqpuRV3FxgrCrY8wHr14TL1ZL2xopl5u2H65eKcq8S\n9IZBRBwn9E8s9h+sUSxqnLydkaYp48GCrd06x0czJmOHJ9+d0hlWkGQYjxacHs3Y3K7z+Ku3xLHg\n7H72pzu8OhiyuVXHmnnUWyUOHvcI/YTp0OHGnQ5Hryf89ldv8BYhmqFw//NNFlaANfMvuPosx+pe\nYpZ1NrcbK1W8d/dslYJzPhk7H2ysbYiiztNvT1AUJRc2um6QI3jLAHq1Ei4hBOG6roqA9jSk2S4R\neILSoxdU6tle6fsRo551KcVsyXUWzwPIEpm9sMN4uMj2/4T1rQq6pp2xL601TIzknXnDEpERe4fF\nZOzgLQJsW1Drjg/fdSi9zE2p0Srl1/U+3/o0gTcHQ2oNk/nUo1Yv5FS+JRWo3irx4nEfzVAIfbHf\nLK2IP0RrWG3GuEyaiiWNtY0qnhtcsGm8TFC+uV3nz/+jfUZ9G1WTuXVvDT+IeP1sTJIkHL2cXOi3\nsPqdHZsz9JGrUDkQnzmfuLx9PeHgcS8P0JaoxfmmgMsAfbmXnBxORPIeJmd6jiyRs9V5kBWJ9ysN\nkQAAIABJREFUNIHHvz1GUSU2t+tImYtbinRGDL1EBZcGBksB+Lsu8Gf1N8vi3uDUPtOzYWe/xZvn\nI2wryKvok+GC0go6cX5vWb73dOrkZ7OmKWKrWtmWfl/h6PneEvv3uvROLZ789gRnEdCfTXh4P8Jz\nQxwryJ5VGVmWWNusoGvqpVbAq+MyVOnwxZjvv35LGMR4TnhmXV91biwpcsO+ReDHpGnK8dEMxwnp\ndMtc26tz9GpCvWheifpcNj4qiJck6U/TNP3lJf/+0zRNf/Ux7/GHHC8PRkyHIjjtvZ3R3aoy7i9y\nvjScXSRmWefabgNJIqerLGyfheVzfDhlMlwwnwl6gO9N2diqv/PmkcA0hZhpFW5aBnKartI/mfHj\nv9gjTcUmuH2zeeGaV4MAs2ywd6tF71g4zMiylL23jqopNNfK9I7mqJrMi6dDGs3yUjdLkiY5n9Uo\nalRqBp4bkaYp9VaJ2cTNg/n51L0gPlzCZo4dcONOm2LZoFI1sOY+5YrBdLRgY0dwhCu1AlHWnKlc\nK/DiSZ+iqYtD7UaLhR1gW16WeES0uxUOX47YokVTlXnw+aaYrBTevp5gzUQG21mvoBtqxi8OKZg6\n476NYwUEXgyUKZdFRVDVZAH9p2CWNEolgyRNef6oh26oTMYL9vY7FE0Nzw0omDqSJNNZr9DulN6b\n2a4+rHf3P8sz4fFgwcnRFFVTmI09Gm2Tu59vYE0FVaCTBQeDE4swiPL1EIUxo77FydEU3VBZLHzu\nPFin2iwS+MJWUNdFBXx5j1aFlJf6dz9czwOW885LRkHJE49XT4f8u3/1VIiAJbj/xSaDEyurfBii\nYVdGBwm8EDtNmQwdsa7SLqWKeF40XSVOElpdYa2aJilGSUVVFWYTF0WWsC2P2w82cB1xHY7jU2sU\nefZdj8CP2H+wjp8FiEmUEPoxYSASo/Z6hWrNYHO7kQdZSZRw8EOfNy+GgMSN3QckScp87gLkOoZl\nF+IllzdNLnZQbq+VcSxfBH0TlzCK6WxWmQ4XjAc29bbJk+96F9x0LqtcnnfrWNJ3kjgBUmaZhaok\nJTx7PMi54oWixqhvUa0XuXlvLX/GD18O2bnZIghEE7XAj/jsx1s4jnCTarTeVR/FPiUqTGkCT745\nYWEH9I5n3Ly7RpqlXRcsMy+hhKwGA6t7o6YLsX/gBwRehKYpFAvvgrfZ1EU3VPau3Rf7ycyju1mh\n062g64qA3P2Ik+cjQKBruiYQhK0bLWQZikUdz3UIfCEaLlcNQj9G1WTBqU9SiiUt432/OxBXK1W2\n5bF/r4teUN7Lo10GZElU4vDlmOOVRoBngo1Mf/H21ZjJwEHRZNY2Kvz6378kTSBNE67ttfLq3Wol\nfKnF6qyLa6g1i6i6QrGsUW+ZOJafaxusuSdsanMUqAuplFVnFVRNorVWRtVUoijl2m6D8dDG9yIe\nf31CFMY0Wvv0j9/x/G+zzn/xX/0lw559IVkZ9W1eHowyq9w+cZzSXi+zlyEWH3J2Wo6rBJmplLKx\n3eDZ96fIskxKSqNdRiLFdUPmMxdZkXGzSr2spOK7Z3acwlXoajH8ajV/2ZTQNPUcpZ5PPHw/ygP6\n4vJ9FgGTkU2rW+bb37xdSXQruRZKBOiX91s4L1pvrZUuRbTOz0cYRNzYeUAcJcwnHoevJphlEbge\nH06ERSYiKQjDmMlokd+zdoai9d6K5olLygqp6FvxNJuHZsekYOrEYczx4YTjN1PiKMld2gCeXlLp\nXXV30g2FKKtCAxf3zWVxgos9G85rozRNPXPfzq+VJIavfvGGgqkxGSzoXhOOcWF27vtexLXdy/tY\nfAzl6nxvCc8N8g71RVPn/voXFAoqxaKGYwXUWyavDkbUGyZJygWWw1l0SMSGw54l9F8zF2vucfPO\nGrYlGkHKspwzIK5K8lbXs235TMeOoGZ3TFwn5PTomKNKka3rjTM0ztXk6n3jYyvx/wq4jGT3/wAX\nI9Q/8liKcmpNk+61Gs22qKKuwoJXecMOT63cX3U2cag1S/RPLXw34vRoRme9jCzD1l6dwyxLOj2Z\n0aXG1m4Df12I8wI/otkyabRKDHsXIbPz4zw/FQlkWcZ3Q4olPQ94XSfgm789ZDJ0UBSZ25+u59QN\nWZbQdIXXL8aYRY0qkMacsR/b2K1TKul0Nioi4AkjSHWcRYDvR5TKOg++2KR/IrLDwI+YjWOe/zBg\nbaPK+pbG9o0GjVYZe+7j2AG+J9rTz8YeoZ9glgQMO5+7bO01sOY+lWqBR799y+6tNvWGyf69bj7n\nrw+GhGHMfOKQJCIw/+nPriMrMlGYIEugaTJ3Pl3H9yLWNqs5BclzAv7k5zdIopRKJrSRFZn2egV7\nLipcSZwwm7o4iwBn4ROGkbDeszyePTpF11RSKc0DgA9BgUtnlmXVqX9isb3XZO9mK7+/aZpyO3vg\nF7boSKuqMkEQI8uC3rO102A29QSvMBZzqGoyO7ealMsG9abJeGBj6IqohqcSR68nLKyAKHKpBkXs\n+TtrSimVLjgvLcdoICp+SZyQpOLwrLfFATDszWl3qzkVY22jwnzqUihqWYC5YgVpe7h2yJsXI14/\nH5GmKevXavhuxPNHPRRFZu9WC3suKGpxlJCkCc8fC42EbqjEYczCCpgMFuL3GeSpZjaBZyupcPRq\nTO94jlkyeHNwjOtGnB5N+ad/eQfwcx1DGIRs3WgJhKVdIjnngrus9h0fTVnfqovnuVthOhT2hpW6\nEEEuX3tZAPFuLXRxrODShkjLxjsg/J8765XciUVWZDw34JMfb/Ho62NKFYHsbW432Nhu8PLpENPU\nsGc+tUYhc7vSz9i3gYD9n357iiRLjAYWnhcKnreirCCOZ8d7g6JmEaOgUijpQEpz7V2/gyRJ0Q0F\nXVM5it8hDLW6Sf90nsHiKXGc0GgVUVSZJKOHRVGEJLXQdZVay+To5Rh77jEeLLjz2QbDnkWpKpyo\n1jaqhFEs3LxSKeujIbGwgjOV3aXQfcsSe7BR1BgNBCJ5FY92NSDwvYivf/kaWZapt0ymE4d6w8wr\n684iQFElzJKOokqoqqjEh36MNRNJe5oklKvmBW2A2G9SIhJ0QxXFjEVAs1PCtjxK1QL1lvgs3wnx\nvYjAj9ANlf6JxauDIbIsE0eCfjMdLnjxQx+9oNJeq9DZqHD8ZpqjbbZ1ScC9VuayUMdZiCJJ4Efo\nBY1muySSJUTxajnOBB5Z4Ljqd3+VILNcLpAkk9xWV88COt+PePmDcJ0yCjrlqoHvhYR+TLNdwiyJ\nQFzVFOZTIQpeWhiuUggWtn9GOF1rykgyOaVkPnWZzzxBt2mZdDcrbO3WmU2F0P7pNyc4i5A4TphO\nHGxL0BxXg+5Vzc0yaBpke3h3s4rrhGztnaVWnV9jS+Sv2SlRqRcZ9S3a62UUVaZ/OmeeMQSCIM72\nRIWCqed6JnjX8fbzP9vh5M0kE0QLHn69+Y4GJikyT747QVUUJsMFG9t1cQ12kCOoq4Luw1cTzEoh\nd3cK/JhS1UA3FNJE2C/7QYiqy3n8MRktaLTOuq+VyqKAkwKNdonxaEGxoGFme9XSrUlQ/NaYjIQh\nyGLhMZ852JZMd6tKrVGk3ioy6i9IYmEHHgSRoBdfYtP6IcrVeXF9oagThqJwpekKlWqRjZ0GGzsN\nhj0L1wmRZag2THw3QlGzhpyaIjRMK/QYVVPyZmnjrOfP6eEcWRLJttg/Emqt4nuTvOVY2OJ9r9/u\nMO4vWN+q8fUvX1NvlfBdm83d2hmb0GQluSq+J8p+bxAvScIsUfwoSZwBQLgJRO/7+z/WWBXlmGWd\n3Zvti5t7yqUbnW2JTcBxAkI/ptYw2b7e4M3zMbouDkizZOSBWeBF6AWVR1+Lxk6OfbH9+WUQyFWe\nvMuF++jJV1SNXeGXXjLYudGis17h8W+PkSQZSRLtw+NMpAciq/7210dsXW8I15SNCqmUvutGF0Q0\nmyaLRUASp8iyxK17XWYzF8NUseceo77Nxlad6cjFtlziJGXnegtVUxj2bNrrZZqtDK7PgrqFFTAd\nO5TKAikIw4h6Q3SA9b2IetPk6XcnOFZA7+38wv0olQ3GQ5vrd9YIw5i1zSpRlOBMM1hy6YjhC1pN\noym4uL4fEfgxmi7g5BdP+iiqzNZek+PXE2wrII0T/vSf3eTaXoPp0KHaEIHxwgrQdBXdUJiMFtQb\npQudKVcz3m+/+w3Nzs/ye+S7IbWGyYsngxx9aK+/S9CW973dLfP6YMSjr9+Khkqk7NxoEccphy9H\njAYLXj4dcP12m1dPBxhFnVJFR9dV3jwfoRkKQSfmzcEYvahiFNRc/Bn4EWHwzp5LVqRLLSaXbj2y\njBByKRJ6QSH0kxyxePzNCQ+/2GL//hrj/oL+iZWvb7Nk5M+LlFkNqZqCqsoUihquE1KtF0nSFH8R\nEIQJnhsxn4okr9UtEWbBev/EplTRGfQtNrdq1Fsma5tVKrUiqirlHWZz2pDlYU1dTg4nFIo6rhMx\nsJ7zyf0fEYZR3hF1rVvOOf++F3L0eiLoRssN3QlxbCHEmwwWVOtFRj0bzw2oVIvoBWHT6bshZsWg\nVH7n739+LciKxLBn5+so8B0qtQK25eVJ3uprwzDGd0NkRSZNU1qdMr4X0r1Ww/cj6vUCL570KBR1\npiOH9WsbvH42pFQROpalo8z5teksAoY9i+3rTRwrEPFWmpxBHEEczGkqmj2RiSIdO8BZBKi6QAvt\nqRBXT0YL0dTqoUQ7CwZty0OWJIJAUOVkWcr4/SWmE4c3J4+5sfOQyBHi1KffnlCqFnj1bEijVcKe\neRklDrrXahQMlda66Cmxs99ClkVPAEWDOIEf/8WeqMiWdOy5x+vnQ6SsOV2jLegAklCkn6lUvQ+6\nXuWeJmmCrChUawZPvzsVc75VY2tPVL58P2I+dbHnHg9/LHpTNNdKzMZOTg1LU4HAbl9vMuov+OGb\nkxwtuf2wy2TkCN54ELGwPPbvr5MmMUVTIwoiBj1B52h0SsLeMAVr5qJpCgff90ACe+axc7OdB8Vh\nECFL0NksU66I561SLWBZLrqh4Wbi4P/7//yX1M0b+Xd/V1GGhSV41PWmyeHLMWZJJ4oSqg3haCYo\no++aDJ4JpFZsLUtlIxe8D08Ft1vNEIvZ1KVY1KjUi2K+k1Ssv96COJ5z97NNAj9CVWVU/V0gHhFz\n/U6HVwdDKpUiR68nZ3RbpKAbKgff93Jb0tsP1vLvGcUx7bUqZkk4Zb18OqS1JgL5w1cTPDdibaPC\nq6dDSlUjp360MjOHQlFlfaeeG0SkiagarzqAKYqM4/h892tBW6w2zIzSKJ7Vw1fjHHXdv7fGo8d/\nx87ufV4diEJasayzca2WBW9tFFlGL6iZgUWa6ztODic5HS4MYzw3yl2clue9piv0384F6yArEs2m\nLpPhglsPuszGblZhTrIzScRGruOTxGJfqLdMhqdzzEqBF4/7NDvvipzLppjuIqTRNPOEaElbGq0g\nAhvXajkNGVKePcqsY52Aze0a04lLEqeMBja1epGXT4eoikyhGEGaRWJphgJ8e4Iiy3h+SHfzrGj5\nMsrV+Wr5xlY9F9e/Phiydb3Jw59sIcsSz19/R7t7Pz+fh6cWcZzw8ukQdxEwn7rs7reYuyGT4YLp\nyMkNQpbFYVF0E+dZwVRptks4js/uzTaBLzQI2zeajAeLMzSm88itmSX81szDnntALU9cZUk44NVb\nxZxV4Trvr8Avx4cq8WKlvft5dSTA//JRn/IHHtf3W5eIms6Oq6qsEhKzmYc9cZmOHcHdC2I++fEW\no6Gdi0TSlDxLKpZ0Abdl9l96QfmgsvoqT97cVcSLiOUUraRglvTcC9c0depN4eMdRjHXdhs0W0UG\nxxau59NaKxEGiUg4/JBmW3z3ZYV2Y0d4ay836EHP4tvfHBH4Ea1uhb39FvOpg6JKNFpCrOg6Ac21\nMtV6ka2dBpDmi3P3ZptRz2Y+dbnzyTq25bO+VSMII6JIBBTPH52ym9EENncbdLpns/pWt8ztBxv8\n8NtjNFXhh6/f8pMvr+e/X7rrmCU9a5/ucfx2jqJIdK9VicIY31PzDc6ae0iyjKrKlBpFrKnLaGgj\nIfPq2YDORhVJEh7+gR8ThYnYuFP9jOfwUmkvKxLIEtOxg235JIlIkA5fTiiagpdZb5YuFeCJxhdk\nPH6V49djtq+3mIwcOutV+qczhDVXyHzq0dQUFEXGKGhsXW9Qrhb4xf97QBQlXL/VxihpbO01ieKU\nVscU1pTZujPLwuZz2eV2SaWQFYlm2+SzP90h8CMhrNMUFnMHWZE5PZoBEgeFPoahkCJhFFVUVXgM\nO7bPZLzg1VNRKVIUoQ/RdIXBqUW5ajCfaVy/1WY8XAh/cz+kVDFwHR/PDdnbb2NNXZxFyHgg6AK9\nY4t6s0i1XhAHUyoOsTRNGfcXuZC2fzKjWi9SrRcZVw2mrgyk1OpntSWvD4YCkVKE8NO2HeqNIm8P\nZ2iazLe/ecPerTXGwwWlqoHnBmiGwvHhhM2dOk9+ewwIFEXsG9UzgjlZkdAzAfW3vz5ia6/B2+zQ\nXtgedz9dz1+7HKqmcPx6SqkivN2XloOlaoGTozmKKvPq+Yj2WiVLyt/ZIWqG6BchdCtS3rBl+RmO\n4zOfejx71OP+jzbRDZU0gVa7RKNtMhkuaK+XcRchT745JgzF87i504BUiOQVWeY3f/US34/QdYUv\n/nwPSM4cmL4nKG3ziYOqKsiKCOJlWWZzp0G7W6HWKFJrFYniBM+LmM+mtNbKzMYLzLLBdOxgmBqP\nvz7m7icbnDwZcOfTdY4mE8HntzzuPFhncDzHsgIUWaLaKOA6IbqhkSQpUZgI1GjtnR3v0lLyfNfm\nVrdy5tD0/Sj3+2+0TOyZh1FQRR8JScJdhGeendnEYe92h8dfH1Mo6EzHC+58usFk6IhKfBbcpsCz\n73tnuM17tzoYhngescGRxDM2nzpsbNXYv79OrWWh6QqHL8d0N6v4Xsje7Q7JyMmqgxJBEANpTnvQ\ndIVK3WT3ZpvZRFAhv/31ITfurPHV37xi/Vod3VDondisPVBzcd0yuUnSlDhKqNQLqKoQSmu6ynTk\ncPRywqhvcfPuGkkqRK2dbgXb9vIAfjZx8Lwwq4ym7O63MQyV50/6jAcLWmslak2T7d0GSrbuRQfh\ngI2dBrPRQiDMisSbg1Hei2M1EA+8CBIR/IVBxOnhlNMVS8B6yzwjgEQWjeXmU4d60+Q3f/2SJKND\n/ejP9wiCCD+IRLI1sFE1hSRNUVWFQlFjOl4w7FmcHs0oVw2syYKtvSbNdolBTwhEwyDC8yJCP0bT\nFXpv5zz7rockS1TrBW7cEW5IJ4dTHCvI14FjB6xdq1GpF1jPAnejoOb3QdUUWp0Sh68mmWvKAtcR\nwaOmKzz59pSdGy1O3k4ZHFvs3W7j2EGO8B++HKOqEmsbVXw/otYoCmrwToM3L4YUijr23Of6rTal\nspUnF54T8dXfvBY8ez/kk59s4WSdi5cMhjRJ8nku1wz6pxaToaAkHx9OAUFnVFQZRZGIoiRDdRJO\nDoVu5vkPfaIoYTSwReHCDVE1mVKlwP0vrvHmxYjpxMGaudz/fBPXEdqXwIuxLQezpDMbL4jChGJG\nWX6f88xyNDulvKA66gs3QUmSuP1wnZFlnqncNzsljt9MIBH3pFQ20HWNdtZJXNfVXI+wLA6bZZ1m\np0S5WiSOU5496vHwR1tUm0WardIZjrymqRRNjbXNKtbMRVJEA8UoSLj9UGgZhz0Le7OKY3ns31+j\nVBUGFxIQBkLLmaZQKCpn6GZXjQ8F8dcRBbl/A/xs5d9TYJCmqfuBv/+jjHYuTHy/oGA1+FludJIM\nO9ebTGtChNM7mtPslNALCj/+8708QJOkNM+SChmcGwVJziH+0LjKk3cpsv3JT/5MCIB0YTeoW2oe\noOzeajMbi3br44ENUkpno4yzCHn2XQ+Q2LnZJI5EN9lru/VMHCUxHQuniKJpYM0dZllTlDCMRcUs\niKnWiiRJysGjE1wnxLUqbOzUaXVKlKv6GZ7t7XQdSUppdkosrABVUzg5nOC6EZVagfnEZXOvSbli\nUK4aGcR00YO1UtfZ2W+zsHx2bjYxyxq943lOVeh0K7SzzPmrXx3y5kC4+Vy/3eH6nQ6GK7xmVU2h\n1izgOTKhJqOoEqWawXhk4zo+dz4VKvg4ThicWNRbJqqmoOkKs6lDkqbMJy5REnPn4Tpbu3X8IGJz\n7XYO8d2426FQ1AQXbi5EuE+/O2Fto0rvrXUBiVkGdVEYs32jxeGrMVIqKDW7+21e/NAX3vqSCPp0\nXaXVLuF7IQvbJ47SvBGZWdR5cThAkcWGeW2vmdvOQTn/rNX1pWoKj74+5uTNjDhO+OKf7DKfCEi3\nUFSp1AzR+EmWmIwdXj4bomsKrhOyvlWj93ZGqVJkNhFQt6RIyBLcvNul3rQoVwsMTi1anTLlaoHH\nX79lNFjQbJvc/ewafkb1CIKIMIwwirrgchZ1ikUNa+7x5Js3gESjY+ZVAscOsOYuiqpQrRd5/Ntj\nru01Wdv8KTs3mhe0JcvvvqQ67e63OXgskKskjtnYaRCFMXu325QrOn/ysxvMxi6ligjSNEPNbdmO\nXo5FE7CVBmu25XP4cow1E1SjhS3W07K9ve8L9K/ZKeWBBSni3spw//NNjIJKrW7y3VdHDE7mpGnK\nzXtdPCfEdQLufNql0a5kHH+ZyXABksS4b0PaZXAyp591N755d03QIFIJZ+7Tt+cUCjqnR1OcRcCL\nZwMk4a5KEMQ4tghUPSegsOzmnFV8DEnD90LcRQCIbsSziYNeEJ7kz74/zV1A7n62seK4U+a//e//\nU148HYomdVNPCPgzu1ghOvVYWEJjFPgRRlE0c1NVib2bbfqnFrqu8OzRKUgyJ6+nyIrE9o0W7fUy\ntuVhlgskUUKSwOGLEd999TZzABJ9N14+FV2bNV3m+u0Ox2+maIbKyeE0tws2THG82ZbHvc82kBWB\nLqRJimW5eRWs1S0zHi548t1Jxon3WL9WQ1Vltm80GfXtFQqHI9Z1ECMrEEcpp28nGIYudEhtkyAI\nKZV1jILKs0d9ru01efN8RLNTQtMUqo0iSWTkSMVk5CDL4j6ZZSOvIhZNnZOjKZPBAmvmsbFTZ3Bi\nMZs6BK6gqj355pRW/TovHvdzcV1uKyiJZm/KTGZztyH86zMzhiAQa2FpWFCpFzh6OWFrryHsP4H5\nzKVtqLx9M8Es61QGC2zbp1oVPG9nEfD8cR9kiesrHbnNsk6zLSq5Ztlg2JufCcRTSehVJqMFvbdz\nhj2bOE6o1Iy84rsUq8uyRG2FTkIi5Y5n0yzxGvUsutdqvHjah1TKK/Z3P90QZ20YUSho1Joms7Er\nxNVBkvedMAoq7fUK40wADxJxGIOESLg7JdIU0qxho58hQYoi0dmoiP1OliiVNb78iy959qjHiyd9\nZEkGKeXep5uMJjbaIqRaL+Rnw/79deYTl4Kp8e1vjlCzintrrYLvRFSqBdrdMo1WiaNXE0hTkjRl\nOl4QBmlOPRJ8bJl6syRoSRLc+WQ9d616ezjJOeyyJItno6ihqCIY753MuPfJJtVGUdBANYXnT/o4\nc5Gg7NwSz2ycpIR+xPffnRLHKevbNbb2mjz/oS9irFOLrd0mjh2gay69kzlrm9WMtiRQV8PUkEjx\nvIAoiDMh/QzXiXCKKnu3b+RuR5BynmNeuiSwXyIV5+lRju3nvv2rbjJRkqIaCv4gyqxHo6xxZEit\nUaS1VmZto0KhoFFvFImihM56maNXE5I4oblWplTVuXW/Cym8eT7i6aNTTo9maJpKrVnEmotqe7lW\npNUxMYoq7iJg+3orv+5ao4BhiLMijWE8XGBbrmiw5UZU61s0OialsoEbW1w13hvEp2n6Ovtx932v\n+8c2VrlqaQKO4+O6Ea4juJarwY9ZMtALKqEXkaQJjXaJzkaFze36mer6wg5yeseob7Nzo5UdUFre\nWOH91xLkXsGqpp6h/yxdR9a6ZVKJXPS4pAbYM48n35wKyklmR7esut1+uI4kS2iqwqO/O0JWlLza\n0e9ZQkWOqHR88qMt4R4gCT6yriuUy7oQiFUMGp0yJS8S76cLTuZ4tMjtm6IwZjS0sGY+9szLKDdz\nojDF90JanTJ27KNmPN3t660rEYpi0SAMRJCZJCm9t/MLQhcQB3DgC59eWRabjrcIMAoK9z7fJElT\ndE1BkiQ0TaXSKGRdYDXCIGE2cbi2W6dQ1KhUDQolnY3tGr4XE/oRB497WDOPWrPI4MRG02T0goaU\nQuCJSsySUgUp7fUqnhOgqAquG2aZ/1lh1CqH1Fn4mEUhHu50KzRaJl/+81v4XsiX//w2siyxsS0S\nJt+PGA9sHv74GuPRgkJJCCJbnXImki7jzF1uZVz21lo5d2dY5QYGXsjGVgNVEV0JVUXmydMB5apI\nZnZvdWjNPbEhWgHbe03+7m9eYc8D5jOX/ftd4jihWivw+Ldv6WxU6b2dc/12m1Hf5vRoRgrUm0Uh\nQh6K5FhRBa9QAoa9kO3rTZ497hP6MbOJS70pejNMRg5pBqlGYcI8g2zHA9FdcTH3MAqtzLEoyHzs\nJcb9BbblQSrhBwFpDJWaeI6v7Tbon1hYUz8TRQYoypwoiuleq1GrFfjrf30g1pIMX/7zO8ThOKvS\nCD7zauV7iahIElTrRX79716yf69LFMUUiiJgq9VEcDEeCAvapUbFdQJK5QJhGNPdqDKbOBhFnZ0b\nLYZ9sRlv7Aj+Y7la4PDlCMcWNISffLlHnKTUWyZhGPHs0ZTvfnNEHCfUmkUefHFNuEllSNHUcTLh\n3JRxb4Gqy3Q3q2iaQsFEBNBVPePOCytSoyBTb5WJooRKo8DC8nn1dCDEdkOHvTsdZFkWCaah5jzp\nnK8sie+ZpjA4nbN/v4vrBHQ3q8xnmfh1vy18mVslXjztE3gxm9s1rLlH/3ieoU9qztHN0tB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vS7DpmcjWlprJ0T5nmhkqJdH9LtjPDdkFufWWc8dnl095Az58p0miM8N0RRRyytF3j4sXRKGkd9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\nTMsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb86ef464e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(12.5, 4)\n",
    "std_height = 15\n",
    "mean_height = 150\n",
    "\n",
    "n_counties = 5000\n",
    "pop_generator = pm.rdiscrete_uniform\n",
    "norm = pm.rnormal\n",
    "\n",
    "# generate some artificial population numbers\n",
    "population = pop_generator(100, 1500, size=n_counties)\n",
    "\n",
    "average_across_county = np.zeros(n_counties)\n",
    "for i in range(n_counties):\n",
    "    # generate some individuals and take the mean\n",
    "    average_across_county[i] = norm(mean_height, 1. / std_height ** 2,\n",
    "                                    size=population[i]).mean()\n",
    "\n",
    "# located the counties with the apparently most extreme average heights.\n",
    "i_min = np.argmin(average_across_county)\n",
    "i_max = np.argmax(average_across_county)\n",
    "\n",
    "# plot population size vs. recorded average\n",
    "plt.scatter(population, average_across_county, alpha=0.5, c=\"#7A68A6\")\n",
    "plt.scatter([population[i_min], population[i_max]],\n",
    "            [average_across_county[i_min], average_across_county[i_max]],\n",
    "            s=60, marker=\"o\", facecolors=\"none\",\n",
    "            edgecolors=\"#A60628\", linewidths=1.5,\n",
    "            label=\"extreme heights\")\n",
    "\n",
    "plt.xlim(100, 1500)\n",
    "plt.title(\"Average height vs. County Population\")\n",
    "plt.xlabel(\"County Population\")\n",
    "plt.ylabel(\"Average height in county\")\n",
    "plt.plot([100, 1500], [150, 150], color=\"k\", label=\"true expected \\\n",
    "height\", ls=\"--\")\n",
    "plt.legend(scatterpoints=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do we observe? *Without accounting for population sizes* we run the risk of making an enormous inference error: if we ignored population size, we would say that the county with the shortest and tallest individuals have been correctly circled. But this inference is wrong for the following reason. These two counties do *not* necessarily have the most extreme heights. The error results from the calculated average of smaller populations not being a good reflection of the true expected value of the population (which in truth should be $\\mu =150$). The sample size/population size/$N$, whatever you wish to call it,  is simply too small to invoke the Law of Large Numbers effectively. \n",
    "\n",
    "We provide more damning evidence against this inference. Recall the population numbers were uniformly distributed over 100 to 1500. Our intuition should tell us that the counties with the most extreme population heights should also be uniformly spread over 100 to 1500, and certainly independent of the county's population. Not so. Below are the population sizes of the counties with the most extreme heights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population sizes of 10 'shortest' counties: \n",
      "[100 103 138 182 194 100 118 161 156 186]\n",
      "\n",
      "Population sizes of 10 'tallest' counties: \n",
      "[100 147 132 193 270 130 414 101 150 109]\n"
     ]
    }
   ],
   "source": [
    "print(\"Population sizes of 10 'shortest' counties: \")\n",
    "print(population[np.argsort(average_across_county)[:10]])\n",
    "print(\"\\nPopulation sizes of 10 'tallest' counties: \")\n",
    "print(population[np.argsort(-average_across_county)[:10]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not at all uniform over 100 to 1500. This is an absolute failure of the Law of Large Numbers. \n",
    "\n",
    "##### Example:  Kaggle's *U.S. Census Return Rate Challenge*\n",
    "\n",
    "Below is data from the 2010 US census, which partitions populations beyond counties to the level of block groups (which are aggregates of city blocks or equivalents). The dataset is from a Kaggle machine learning competition some colleagues and I participated in. The objective was to predict the census letter mail-back rate of a group block, measured between 0 and 100, using census variables (median income, number of females in the block-group, number of trailer parks, average number of children etc.). Below we plot the census mail-back rate versus block group population:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vi+dbDIViPxakET+TN2GqlzubLjBbHPLE9qVimUK+PO7lPtC9c+lzun4iUT8r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O7eyCr6aQqF0gJWXmfzbpfeNG11frnMeOjhyAKzC0UESaQxl6e9P2I88JFkAfLZ9e1sDqnP5tnf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\n4w7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SiIiIhFCOPvFP5P0+BvxSmcZWNSvVlq+HmbOtZwvpiwu0tDbS198OGLMzc8ylF3P3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\nwWgSLyIiIiKywfz/x4zZNvAVNBcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb86ef52668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(12.5, 6.5)\n",
    "data = np.genfromtxt(\"./data/census_data.csv\", skip_header=1,\n",
    "                     delimiter=\",\")\n",
    "plt.scatter(data[:, 1], data[:, 0], alpha=0.5, c=\"#7A68A6\")\n",
    "plt.title(\"Census mail-back rate vs Population\")\n",
    "plt.ylabel(\"Mail-back rate\")\n",
    "plt.xlabel(\"population of block-group\")\n",
    "plt.xlim(-100, 15e3)\n",
    "plt.ylim(-5, 105)\n",
    "\n",
    "i_min = np.argmin(data[:, 0])\n",
    "i_max = np.argmax(data[:, 0])\n",
    "\n",
    "plt.scatter([data[i_min, 1], data[i_max, 1]],\n",
    "            [data[i_min, 0], data[i_max, 0]],\n",
    "            s=60, marker=\"o\", facecolors=\"none\",\n",
    "            edgecolors=\"#A60628\", linewidths=1.5,\n",
    "            label=\"most extreme points\")\n",
    "\n",
    "plt.legend(scatterpoints=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is a classic phenomenon in statistics. I say *classic* referring to the \"shape\" of the scatter plot above. It follows a classic triangular form, that tightens as we increase the sample size (as the Law of Large Numbers becomes more exact). \n",
    "\n",
    "I am perhaps overstressing the point and maybe I should have titled the book *\"You don't have big data problems!\"*, but here again is an example of the trouble with *small datasets*, not big ones. Simply, small datasets cannot be processed using the Law of Large Numbers. Compare with applying the Law without hassle to big datasets (ex. big data). I mentioned earlier that paradoxically big data prediction problems are solved by relatively simple algorithms. The paradox is partially resolved by understanding that the Law of Large Numbers creates solutions that are *stable*, i.e. adding or subtracting a few data points will not affect the solution much. On the other hand, adding or removing data points to a small dataset can create very different results. \n",
    "\n",
    "For further reading on the hidden dangers of the Law of Large Numbers, I would highly recommend the excellent manuscript [The Most Dangerous Equation](http://nsm.uh.edu/~dgraur/niv/TheMostDangerousEquation.pdf). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example: How to order Reddit submissions\n",
    "\n",
    "You may have disagreed with the original statement that the Law of Large numbers is known to everyone, but only implicitly in our subconscious decision making. Consider ratings on online products: how often do you trust an average 5-star rating if there is only 1 reviewer? 2 reviewers? 3 reviewers? We implicitly understand that with such few reviewers that the average rating is **not** a good reflection of the true value of the product.\n",
    "\n",
    "This has created flaws in how we sort items, and more generally, how we compare items. Many people have realized that sorting online search results by their rating, whether the objects be books, videos, or online comments, return poor results. Often the seemingly top videos or comments have perfect ratings only from a few enthusiastic fans, and truly more quality videos or comments are hidden in later pages with *falsely-substandard* ratings of around 4.8. How can we correct this?\n",
    "\n",
    "Consider the popular site Reddit (I purposefully did not link to the website as you would never come back). The site hosts links to stories or images, and a very popular part of the site are the comments associated with each link. Redditors can vote up or down on each submission (called upvotes and downvotes). Reddit, by default, will sort submissions to a given subreddit by Hot, that is, the submissions that have the most upvotes recently.\n",
    "\n",
    "<img src=\"http://i.imgur.com/3v6bz9f.png\" />\n",
    "\n",
    "\n",
    "How would you determine which submissions are the best? There are a number of ways to achieve this:\n",
    "\n",
    "1. *Popularity*: A submission is considered good if it has many upvotes. A problem with this model is that a submission with hundreds of upvotes, but thousands of downvotes. While being very popular, the submission is likely more controversial than best.\n",
    "2. *Difference*: Using the *difference* of upvotes and downvotes. This solves the above problem, but fails when we consider the temporal nature of submission. Depending on when a submission is posted, the website may be experiencing high or low traffic. The difference method will bias the Top submissions to be the those made during high traffic periods, which have accumulated more upvotes than submissions that were not so graced, but are not necessarily the best.\n",
    "3. *Time adjusted*: Consider using Difference divided by the age of the submission. This creates a *rate*, something like *difference per second*, or *per minute*. An immediate counter-example is, if we use per second, a 1 second old submission with 1 upvote would be better than a 100 second old submission with 99 upvotes. One can avoid this by only considering at least t second old submission. But what is a good t value? Does this mean no submission younger than t is good? We end up comparing unstable quantities with stable quantities (young vs. old submissions).\n",
    "3. *Ratio*: Rank submissions by the ratio of upvotes to total number of votes (upvotes plus downvotes). This solves the temporal issue, such that new submissions who score well can be considered Top just as likely as older submissions, provided they have many upvotes to total votes. The problem here is that a submission with a single upvote (ratio = 1.0) will beat a submission with 999 upvotes and 1 downvote (ratio = 0.999), but clearly the latter submission is *more likely* to be better.\n",
    "\n",
    "I used the phrase *more likely* for good reason. It is possible that the former submission, with a single upvote, is in fact a better submission than the latter with 999 upvotes. The hesitation to agree with this is because we have not seen the other 999 potential votes the former submission might get. Perhaps it will achieve an additional 999 upvotes and 0 downvotes and be considered better than the latter, though not likely.\n",
    "\n",
    "What we really want is an estimate of the *true upvote ratio*. Note that the true upvote ratio is not the same as the observed upvote ratio: the true upvote ratio is hidden, and we only observe upvotes vs. downvotes (one can think of the true upvote ratio as \"what is the underlying probability someone gives this submission a upvote, versus a downvote\"). So the 999 upvote/1 downvote submission probably has a true upvote ratio close to 1, which we can assert with confidence thanks to the Law of Large Numbers, but on the other hand we are much less certain about the true upvote ratio of the submission with only a single upvote. Sounds like a Bayesian problem to me.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to determine a prior on the upvote ratio is to look at the historical distribution of upvote ratios. This can be accomplished by scraping Reddit's submissions and determining a distribution. There are a few problems with this technique though:\n",
    "\n",
    "1. Skewed data:  The vast majority of submissions have very few votes, hence there will be many submissions with ratios near the extremes (see the \"triangular plot\" in the above Kaggle dataset), effectively skewing our distribution to the extremes. One could try to only use submissions with votes greater than some threshold. Again, problems are encountered. There is a tradeoff between number of submissions available to use and a higher threshold with associated ratio precision. \n",
    "2. Biased data: Reddit is composed of different subpages, called subreddits. Two examples are *r/aww*, which posts pics of cute animals, and *r/politics*. It is very likely that the user behaviour towards submissions of these two subreddits are very different: visitors are likely to be more friendly and affectionate in the former, and would therefore upvote submissions more, compared to the latter, where submissions are likely to be controversial and disagreed upon. Therefore not all submissions are the same. \n",
    "\n",
    "\n",
    "In light of these, I think it is better to use a `Uniform` prior.\n",
    "\n",
    "\n",
    "With our prior in place, we can find the posterior of the true upvote ratio. The Python script `top_showerthoughts_submissions.py` will scrape the best posts from the `showerthoughts` community on Reddit. This is a text-only community so the title of each post *is* the post. Below is the top post as well as some other sample posts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Post contents: \n",
      "\n",
      "Toilet paper should be free and have advertising printed on it.\n"
     ]
    }
   ],
   "source": [
    "# adding a number to the end of the %run call will get the ith top post.\n",
    "%run top_showerthoughts_submissions.py 2\n",
    "\n",
    "print(\"Post contents: \\n\")\n",
    "print(top_post)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Some Submissions (out of 98 total) \n",
      "-----------\n",
      "\"You will never feel how long time is until you have allergies and snot slowly dripping out of your nostrils, while sitting in a classroom with no tissues.\"\n",
      "upvotes/downvotes:  [71  6] \n",
      "\n",
      "\"What if porn ads weren't fake and all these years I've been missing out on these local mums in my area that want to fuck?\"\n",
      "upvotes/downvotes:  [43 11] \n",
      "\n",
      "\"You'll be real lucky to find a Penny in Canada.\"\n",
      "upvotes/downvotes:  [28 11] \n",
      "\n",
      "\"\"Smells Like Teen Spirit\" is as old to listeners of today as \"Yellow Submarine\" was to listeners of 1991.\"\n",
      "upvotes/downvotes:  [92 10] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "contents: an array of the text from the last 100 top submissions to a subreddit\n",
    "votes: a 2d numpy array of upvotes, downvotes for each submission.\n",
    "\"\"\"\n",
    "n_submissions = len(votes)\n",
    "submissions = np.random.randint( n_submissions, size=4)\n",
    "print(\"Some Submissions (out of %d total) \\n-----------\"%n_submissions)\n",
    "for i in submissions:\n",
    "    print('\"' + contents[i] + '\"')\n",
    "    print(\"upvotes/downvotes: \",votes[i,:], \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " For a given true upvote ratio $p$ and $N$ votes, the number of upvotes will look like a Binomial random variable with parameters $p$ and $N$. (This is because of the equivalence between upvote ratio and probability of upvoting versus downvoting, out of $N$ possible votes/trials). We create a function that performs Bayesian inference on $p$, for a particular comment's upvote/downvote pair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymc as pm\n",
    "\n",
    "\n",
    "def posterior_upvote_ratio(upvotes, downvotes, samples=20000):\n",
    "    \"\"\"\n",
    "    This function accepts the number of upvotes and downvotes a particular submission received, \n",
    "    and the number of posterior samples to return to the user. Assumes a uniform prior.\n",
    "    \"\"\"\n",
    "    N = upvotes + downvotes\n",
    "    upvote_ratio = pm.Uniform(\"upvote_ratio\", 0, 1)\n",
    "    observations = pm.Binomial(\"obs\", N, upvote_ratio, value=upvotes, observed=True)\n",
    "    # do the fitting; first do a MAP as it is cheap and useful.\n",
    "    map_ = pm.MAP([upvote_ratio, observations]).fit()\n",
    "    mcmc = pm.MCMC([upvote_ratio, observations])\n",
    "    mcmc.sample(samples, samples / 4)\n",
    "    return mcmc.trace(\"upvote_ratio\")[:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are the resulting posterior distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'figsize' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-53899899b283>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfigsize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m11.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mposteriors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mcolours\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"#348ABD\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"#A60628\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"#7A68A6\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"#467821\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"#CF4457\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubmissions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubmissions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'figsize' is not defined"
     ]
    }
   ],
   "source": [
    "figsize(11., 8)\n",
    "posteriors = []\n",
    "colours = [\"#348ABD\", \"#A60628\", \"#7A68A6\", \"#467821\", \"#CF4457\"]\n",
    "for i in range(len(submissions)):\n",
    "    j = submissions[i]\n",
    "    posteriors.append(posterior_upvote_ratio(votes[j, 0], votes[j, 1]))\n",
    "    plt.hist(posteriors[i], bins=18, normed=True, alpha=.9,\n",
    "             histtype=\"step\", color=colours[i % 5], lw=3,\n",
    "             label='(%d up:%d down)\\n%s...' % (votes[j, 0], votes[j, 1], contents[j][:50]))\n",
    "    plt.hist(posteriors[i], bins=18, normed=True, alpha=.2,\n",
    "             histtype=\"stepfilled\", color=colours[i], lw=3, )\n",
    "\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlim(0, 1)\n",
    "plt.title(\"Posterior distributions of upvote ratios on different submissions\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some distributions are very tight, others have very long tails (relatively speaking), expressing our uncertainty with what the true upvote ratio might be.\n",
    "\n",
    "### Sorting!\n",
    "\n",
    "We have been ignoring the goal of this exercise: how do we sort the submissions from *best to worst*? Of course, we cannot sort distributions, we must sort scalar numbers. There are many ways to distill a distribution down to a scalar: expressing the distribution through its expected value, or mean, is one way. Choosing the mean is a bad choice though. This is because the mean does not take into account the uncertainty of distributions.\n",
    "\n",
    "I  suggest using the *95% least plausible value*, defined as the value such that there is only a 5% chance the true parameter is lower (think of the lower bound on the 95% credible region). Below are the posterior distributions with the 95% least-plausible value plotted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 3 2] [0.95553912986585299, 0.94130501756135543, 0.80681345969724116, 0.88775207639838272]\n"
     ]
    },
    {
     "data": {
      "image/png": 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rcampqYwaNQpXV1eCgoI4evSoFnf27Fm6dOmCu7s7QUFB/Pbbbwby6Q+h5TQs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IyLmRgXM7EGsvoxA0lEREREjnAtbJ9i3ZC5GBszMS5mYlzMxBrI6scMJBERERE5whpIn2LdkLkY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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb86ea8af60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = posteriors[0].shape[0]\n",
    "lower_limits = []\n",
    "\n",
    "for i in range(len(submissions)):\n",
    "    j = submissions[i]\n",
    "    plt.hist(posteriors[i], bins=20, normed=True, alpha=.9,\n",
    "             histtype=\"step\", color=colours[i], lw=3,\n",
    "             label='(%d up:%d down)\\n%s...' % (votes[j, 0], votes[j, 1], contents[j][:50]))\n",
    "    plt.hist(posteriors[i], bins=20, normed=True, alpha=.2,\n",
    "             histtype=\"stepfilled\", color=colours[i], lw=3, )\n",
    "    v = np.sort(posteriors[i])[int(0.05 * N)]\n",
    "    # plt.vlines( v, 0, 15 , color = \"k\", alpha = 1, linewidths=3 )\n",
    "    plt.vlines(v, 0, 10, color=colours[i], linestyles=\"--\", linewidths=3)\n",
    "    lower_limits.append(v)\n",
    "    plt.legend(loc=\"upper left\")\n",
    "\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.title(\"Posterior distributions of upvote ratios on different submissions\");\n",
    "order = np.argsort(-np.array(lower_limits))\n",
    "print(order, lower_limits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best submissions, according to our procedure, are the submissions that are *most-likely* to score a high percentage of upvotes. Visually those are the submissions with the 95% least plausible value close to 1.\n",
    "\n",
    "Why is sorting based on this quantity a good idea? By ordering by the 95% least plausible value, we are being the most conservative with what we think is best.  When using the lower-bound of the 95% credible interval, we believe with high certainty that the 'true upvote ratio' is at the very least equal to this value (or greater), thereby ensuring that the best submissions are still on top. Under this ordering, we impose the following very natural properties:\n",
    "\n",
    "1. given two submissions with the same observed upvote ratio, we will assign the submission with more votes as better (since we are more confident it has a higher ratio).\n",
    "2. given two submissions with the same number of votes, we still assign the submission with more upvotes as *better*.\n",
    "\n",
    "### But this is too slow for real-time!\n",
    "\n",
    "I agree, computing the posterior of every submission takes a long time, and by the time you have computed it, likely the data has changed. I delay the mathematics to the appendix, but I suggest using the following formula to compute the lower bound very fast.\n",
    "\n",
    "$$ \\frac{a}{a + b} - 1.65\\sqrt{ \\frac{ab}{ (a+b)^2(a + b +1 ) } }$$\n",
    "\n",
    "where \n",
    "\\begin{align}\n",
    "& a = 1 + u \\\\\\\\\n",
    "& b = 1 + d \\\\\\\\\n",
    "\\end{align}\n",
    "\n",
    "$u$ is the number of upvotes, and $d$ is the number of downvotes. The formula is a shortcut in Bayesian inference, which will be further explained in Chapter 6 when we discuss priors in more detail.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approximate lower bounds:\n",
      "[ 0.9335036   0.95310536  0.94166971  0.90854227  0.88683909  0.85564276\n",
      "  0.85607414  0.93758888  0.95697574  0.91015237  0.9112593   0.91305389\n",
      "  0.91341024  0.83335231  0.87543995  0.87081169  0.92748782  0.90747915\n",
      "  0.89063214  0.89804044  0.91295322  0.78329196  0.91901344  0.79950031\n",
      "  0.84776174  0.83540757  0.77406294  0.81391583  0.7296015   0.79338766\n",
      "  0.82895671  0.85331368  0.81849519  0.72362912  0.83662174  0.81019924\n",
      "  0.78564811  0.84570434  0.8400282   0.76944053  0.85827725  0.74417233\n",
      "  0.8189683   0.8027221   0.79190256  0.9033107   0.81639188  0.76627386\n",
      "  0.8010596   0.63657302  0.62988646  0.75041771  0.85355829  0.84522753\n",
      "  0.75627191  0.8458571   0.80877728  0.66764706  0.69623887  0.71480224\n",
      "  0.72921035  0.86797314  0.73955911  0.90742546  0.80364062  0.72331349\n",
      "  0.79249393  0.72708753  0.81109538  0.66235556  0.80480879  0.72039455\n",
      "  0.73945971  0.83846154  0.69        0.70597731  0.68175931  0.59412132\n",
      "  0.6011942   0.73158407  0.69121436  0.68134548  0.87746603  0.79809005\n",
      "  0.6296728   0.87152685  0.81814153  0.86498277  0.81018384  0.54207776\n",
      "  0.6296728   0.74107856  0.53025484  0.71034959  0.80149882  0.85773646\n",
      "  0.58343356  0.62971097]\n",
      "\n",
      "\n",
      "Top 40 Sorted according to approximate lower bounds:\n",
      "\n",
      "\n",
      "586 18 Someone should develop an AI specifically for reading Terms & Conditions and flagging dubious parts.\n",
      "-------------\n",
      "2354 98 Porn is the only industry where it is not only acceptable but standard to separate people based on race, sex and sexual preference.\n",
      "-------------\n",
      "1924 101 All polls are biased towards people who are willing to take polls\n",
      "-------------\n",
      "949 50 They should charge less for drinks in the drive-thru because you can't refill them.\n",
      "-------------\n",
      "3726 238 When I was in elementary school and going through the DARE program, I was positive a gang of older kids was going to corner me and force me to smoke pot. Then I became an adult and realized nobody is giving free drugs to somebody that doesn't want them.\n",
      "-------------\n",
      "164 7 \"Noted\" is the professional way of saying \"K\".\n",
      "-------------\n",
      "100 4 The best answer to the interview question \"What is your greatest weakness?\" is \"interviews\".\n",
      "-------------\n",
      "267 17 At some point every parent has stopped wiping their child's butt and hoped for the best.\n",
      "-------------\n",
      "291 19 You've been doing weird cameos in your friends' dreams since kindergarten.\n",
      "-------------\n",
      "121 6 Is it really fair to say a person over 85 has heart failure? Technically, that heart has done exceptionally well.\n",
      "-------------\n",
      "523 39 I wonder if America's internet is censored in a similar way that North Korea's is, but we have no idea of it happening.\n",
      "-------------\n",
      "539 41 It's surreal to think that the sun and moon and stars we gaze up at are the same objects that have been observed for millenia, by everyone in the history of humanity from cavemen to Aristotle to Jesus to George Washington.\n",
      "-------------\n",
      "1509 131 Kenny's family is poor because they're always paying for his funeral.\n",
      "-------------\n",
      "164 10 Black hair ties are probably the most popular bracelets in the world.\n",
      "-------------\n",
      "26 0 Now that I am a parent of multiple children I have realized that my parents were lying through their teeth when they said they didn't have a favorite.\n",
      "-------------\n",
      "41 1 If I was as careful with my whole paycheck as I am with my last $20 I'd be a whole lot better off\n",
      "-------------\n",
      "125 8 Surfing the internet without ads feels like a summer evening without mosquitoes\n",
      "-------------\n",
      "157 12 I wonder if Superman ever put a pair of glasses on Lois Lane's dog, and she was like \"what's this Clark? Did you get me a new dog?\"\n",
      "-------------\n",
      "1411 157 My life is really like Rihanna's song, \"just work work work work work\" and the rest of it I can't really understand.\n",
      "-------------\n",
      "19 0 Binoculars are like walkie talkies for the deaf.\n",
      "-------------\n",
      "221 22 I'm honestly slightly concerned how often Reddit commenters make me laugh compared to my real life friends.\n",
      "-------------\n",
      "18 0 Living on the coast is having the window seat of the land you live on.\n",
      "-------------\n",
      "188 19 I have not been thankful enough in the last few years that the Black Eyed Peas are no longer ever on the radio\n",
      "-------------\n",
      "29 1 Rewatching Mr. Bean, I've realised that the character is an eccentric genius and not a blithering idiot.\n",
      "-------------\n",
      "17 0 Sitting on a cold toilet seat or a warm toilet seat both suck for different reasons.\n",
      "-------------\n",
      "54 4 You will never feel how long time is until you have allergies and snot slowly dripping out of your nostrils, while sitting in a classroom with no tissues.\n",
      "-------------\n",
      "16 0 I sneer at people who read tabloids, but every time I look someone up on Wikipedia the first thing I look for is what controversies they've been involved in.\n",
      "-------------\n",
      "1485 222 Kid's menus at restaurants should be smaller portions of the same adult dishes at lower prices and not the junk food that they usually offer.\n",
      "-------------\n",
      "1417 212 Eventually once all phones are waterproof we'll be able to push people into pools again\n",
      "-------------\n",
      "35 2 Childhood and adolescence are thinking that no one has ever felt the way you do and that no one has ever experienced the things that you have. Adulthood is realizing that almost everyone has felt and experienced something similar.\n",
      "-------------\n",
      "60 5 Myspace is so outdated that jokes about it being outdated has become outdated\n",
      "-------------\n",
      "87 9 Yahoo!® is the RadioShack® of the Internet.\n",
      "-------------\n",
      "33 2 People who \"tell it like it is\" rarely do so to say something nice\n",
      "-------------\n",
      "49 4 The world must have been a spookier place altogether when candles and gas lamps were the only sources of light at night besides the moon and the stars.\n",
      "-------------\n",
      "41 3 Closing your eyes after turning off your alarm is a very dangerous game.\n",
      "-------------\n",
      "47 4 As a kid, seeing someone step on a banana peel and not slip was a disappointment.\n",
      "-------------\n",
      "23 1 The phonebook was the biggest invasion of privacy that everyone was oddly ok with.\n",
      "-------------\n",
      "53 5 I'm actually the most productive when I procrastinate because I'm doing everything I possibly can to avoid the main task at hand.\n",
      "-------------\n",
      "86 10 \"Smells Like Teen Spirit\" is as old to listeners of today as \"Yellow Submarine\" was to listeners of 1991.\n",
      "-------------\n",
      "240 36 if an ocean didnt stop immigrants from coming to America what makes us think a wall will?\n",
      "-------------\n"
     ]
    }
   ],
   "source": [
    "def intervals(u, d):\n",
    "    a = 1. + u\n",
    "    b = 1. + d\n",
    "    mu = a / (a + b)\n",
    "    std_err = 1.65 * np.sqrt((a * b) / ((a + b) ** 2 * (a + b + 1.)))\n",
    "    return (mu, std_err)\n",
    "\n",
    "print(\"Approximate lower bounds:\")\n",
    "posterior_mean, std_err = intervals(votes[:, 0], votes[:, 1])\n",
    "lb = posterior_mean - std_err\n",
    "print(lb)\n",
    "print(\"\\n\")\n",
    "print(\"Top 40 Sorted according to approximate lower bounds:\")\n",
    "print(\"\\n\")\n",
    "order = np.argsort(-lb)\n",
    "ordered_contents = []\n",
    "for i in order[:40]:\n",
    "    ordered_contents.append(contents[i])\n",
    "    print(votes[i, 0], votes[i, 1], contents[i])\n",
    "    print(\"-------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can view the ordering visually by plotting the posterior mean and bounds, and sorting by the lower bound. In the plot below, notice that the left error-bar is sorted (as we suggested this is the best way to determine an ordering), so the means, indicated by dots, do not follow any strong pattern. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Rh/U5yn2NEJ0aQYhA/W+MZBwL/ASYaGb3ZNWZzIZIyaTopzvjvbuAB6IP68eT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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb86ea95780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r_order = order[::-1][-40:]\n",
    "plt.errorbar(posterior_mean[r_order], np.arange(len(r_order)),\n",
    "             xerr=std_err[r_order], capsize=0, fmt=\"o\",\n",
    "             color=\"#7A68A6\")\n",
    "plt.xlim(0.3, 1)\n",
    "plt.yticks(np.arange(len(r_order) - 1, -1, -1), map(lambda x: x[:30].replace(\"\\n\", \"\"), ordered_contents));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the graphic above, you can see why sorting by mean would be sub-optimal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extension to Starred rating systems\n",
    "\n",
    "The above procedure works well for upvote-downvotes schemes, but what about systems that use star ratings, e.g. 5 star rating systems. Similar problems apply with simply taking the average: an item with two perfect ratings would beat an item with thousands of perfect ratings, but a single sub-perfect rating. \n",
    "\n",
    "\n",
    "We can consider the upvote-downvote problem above as binary: 0 is a downvote, 1 if an upvote. A $N$-star rating system can be seen as a more continuous version of above, and we can set $n$ stars rewarded is equivalent to rewarding $\\frac{n}{N}$. For example, in a 5-star system, a 2 star rating corresponds to 0.4. A perfect rating is a 1. We can use the same formula as before, but with $a,b$ defined differently:\n",
    "\n",
    "\n",
    "$$ \\frac{a}{a + b} - 1.65\\sqrt{ \\frac{ab}{ (a+b)^2(a + b +1 ) } }$$\n",
    "\n",
    "where \n",
    "\n",
    "\\begin{align}\n",
    "& a = 1 + S \\\\\\\\\n",
    "& b = 1 + N - S \\\\\\\\\n",
    "\\end{align}\n",
    "\n",
    "where $N$ is the number of users who rated, and $S$ is the sum of all the ratings, under the equivalence scheme mentioned above. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example: Counting Github stars\n",
    "\n",
    "What is the average number of stars a Github repository has? How would you calculate this? There are over 6 million repositories, so there is more than enough data to invoke the Law of Large numbers. Let's start pulling some data. TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "While the Law of Large Numbers is cool, it is only true so much as its name implies: with large sample sizes only. We have seen how our inference can be affected by not considering *how the data is shaped*. \n",
    "\n",
    "1. By (cheaply) drawing many samples from the posterior distributions, we can ensure that the Law of Large Number applies as we approximate expected values (which we will do in the next chapter).\n",
    "\n",
    "2. Bayesian inference understands that with small sample sizes, we can observe wild randomness. Our posterior distribution will reflect this by being more spread rather than tightly concentrated. Thus, our inference should be correctable.\n",
    "\n",
    "3. There are major implications of not considering the sample size, and trying to sort objects that are unstable leads to pathological orderings. The method provided above solves this problem.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appendix\n",
    "\n",
    "##### Derivation of sorting comments formula\n",
    "\n",
    "Basically what we are doing is using a Beta prior (with parameters $a=1, b=1$, which is a uniform distribution), and using a Binomial likelihood with observations $u, N = u+d$. This means our posterior is a Beta distribution with parameters $a' = 1 + u, b' = 1 + (N - u) = 1+d$. We then need to find the value, $x$, such that 0.05 probability is less than $x$. This is usually done by inverting the CDF ([Cumulative Distribution Function](http://en.wikipedia.org/wiki/Cumulative_Distribution_Function)), but the CDF of the beta, for integer parameters, is known but is a large sum [3]. \n",
    "\n",
    "We instead use a Normal approximation. The mean of the Beta is $\\mu = a'/(a'+b')$ and the variance is \n",
    "\n",
    "$$\\sigma^2 = \\frac{a'b'}{ (a' + b')^2(a'+b'+1) }$$\n",
    "\n",
    "Hence we solve the following equation for $x$ and have an approximate lower bound. \n",
    "\n",
    "$$ 0.05 = \\Phi\\left( \\frac{(x - \\mu)}{\\sigma}\\right) $$ \n",
    "\n",
    "$\\Phi$ being the [cumulative distribution for the normal distribution](http://en.wikipedia.org/wiki/Normal_distribution#Cumulative_distribution)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercises\n",
    "\n",
    "1\\. How would you estimate the quantity $E\\left[ \\cos{X} \\right]$, where $X \\sim \\text{Exp}(4)$? What about $E\\left[ \\cos{X} | X \\lt 1\\right]$, i.e. the expected value *given* we know $X$ is less than 1? Would you need more samples than the original samples size to be equally accurate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enter code here\n",
    "import scipy.stats as stats\n",
    "exp = stats.expon(scale=4)\n",
    "N = int(1e5)\n",
    "X = exp.rvs(N)\n",
    "# ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2\\. The following table was located in the paper \"Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression\" [2]. The table ranks football field-goal kickers by their percent of non-misses. What mistake have the researchers made?\n",
    "\n",
    "-----\n",
    "\n",
    "####  Kicker Careers Ranked by Make Percentage\n",
    "<table><tbody><tr><th>Rank </th><th>Kicker </th><th>Make % </th><th>Number  of Kicks</th></tr><tr><td>1 </td><td>Garrett Hartley </td><td>87.7 </td><td>57</td></tr><tr><td>2</td><td> Matt Stover </td><td>86.8 </td><td>335</td></tr><tr><td>3 </td><td>Robbie Gould </td><td>86.2 </td><td>224</td></tr><tr><td>4 </td><td>Rob Bironas </td><td>86.1 </td><td>223</td></tr><tr><td>5</td><td> Shayne Graham </td><td>85.4 </td><td>254</td></tr><tr><td>… </td><td>… </td><td>…</td><td> </td></tr><tr><td>51</td><td> Dave Rayner </td><td>72.2 </td><td>90</td></tr><tr><td>52</td><td> Nick Novak </td><td>71.9 </td><td>64</td></tr><tr><td>53 </td><td>Tim Seder </td><td>71.0 </td><td>62</td></tr><tr><td>54 </td><td>Jose Cortez </td><td>70.7</td><td> 75</td></tr><tr><td>55 </td><td>Wade Richey </td><td>66.1</td><td> 56</td></tr></tbody></table>"
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    "In August 2013, [a popular post](http://bpodgursky.wordpress.com/2013/08/21/average-income-per-programming-language/) on the average income per programmer of different languages was trending. Here's the summary chart: (reproduced without permission, cause when you lie with stats, you gunna get the hammer). What do you notice about the extremes?\n",
    "\n",
    "------\n",
    "\n",
    "#### Average household income by programming language\n",
    "\n",
    "<table >\n",
    " <tr><td>Language</td><td>Average Household Income ($)</td><td>Data Points</td></tr>\n",
    " <tr><td>Puppet</td><td>87,589.29</td><td>112</td></tr>\n",
    " <tr><td>Haskell</td><td>89,973.82</td><td>191</td></tr>\n",
    " <tr><td>PHP</td><td>94,031.19</td><td>978</td></tr>\n",
    " <tr><td>CoffeeScript</td><td>94,890.80</td><td>435</td></tr>\n",
    " <tr><td>VimL</td><td>94,967.11</td><td>532</td></tr>\n",
    " <tr><td>Shell</td><td>96,930.54</td><td>979</td></tr>\n",
    " <tr><td>Lua</td><td>96,930.69</td><td>101</td></tr>\n",
    " <tr><td>Erlang</td><td>97,306.55</td><td>168</td></tr>\n",
    " <tr><td>Clojure</td><td>97,500.00</td><td>269</td></tr>\n",
    " <tr><td>Python</td><td>97,578.87</td><td>2314</td></tr>\n",
    " <tr><td>JavaScript</td><td>97,598.75</td><td>3443</td></tr>\n",
    " <tr><td>Emacs Lisp</td><td>97,774.65</td><td>355</td></tr>\n",
    " <tr><td>C#</td><td>97,823.31</td><td>665</td></tr>\n",
    " <tr><td>Ruby</td><td>98,238.74</td><td>3242</td></tr>\n",
    " <tr><td>C++</td><td>99,147.93</td><td>845</td></tr>\n",
    " <tr><td>CSS</td><td>99,881.40</td><td>527</td></tr>\n",
    " <tr><td>Perl</td><td>100,295.45</td><td>990</td></tr>\n",
    " <tr><td>C</td><td>100,766.51</td><td>2120</td></tr>\n",
    " <tr><td>Go</td><td>101,158.01</td><td>231</td></tr>\n",
    " <tr><td>Scala</td><td>101,460.91</td><td>243</td></tr>\n",
    " <tr><td>ColdFusion</td><td>101,536.70</td><td>109</td></tr>\n",
    " <tr><td>Objective-C</td><td>101,801.60</td><td>562</td></tr>\n",
    " <tr><td>Groovy</td><td>102,650.86</td><td>116</td></tr>\n",
    " <tr><td>Java</td><td>103,179.39</td><td>1402</td></tr>\n",
    " <tr><td>XSLT</td><td>106,199.19</td><td>123</td></tr>\n",
    " <tr><td>ActionScript</td><td>108,119.47</td><td>113</td></tr>\n",
    "</table>"
   ]
  },
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    "### References\n",
    "\n",
    "1. Wainer, Howard. *The Most Dangerous Equation*. American Scientist, Volume 95.\n",
    "2. Clarck, Torin K., Aaron W. Johnson, and Alexander J. Stimpson. \"Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression.\" (2013): n. page. [Web](http://www.sloansportsconference.com/wp-content/uploads/2013/Going%20for%20Three%20Predicting%20the%20Likelihood%20of%20Field%20Goal%20Success%20with%20Logistic%20Regression.pdf). 20 Feb. 2013.\n",
    "3. http://en.wikipedia.org/wiki/Beta_function#Incomplete_beta_function"
   ]
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    "from IPython.core.display import HTML\n",
    "\n",
    "\n",
    "def css_styling():\n",
    "    styles = open(\"../styles/custom.css\", \"r\").read()\n",
    "    return HTML(styles)\n",
    "css_styling()"
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